# Explaining Reputation Assessments

Ingrid Nunes<EMAIL_ADDRESS>Phillip Taylor
<EMAIL_ADDRESS>Lina Barakat<EMAIL_ADDRESS>Nathan
Griffiths<EMAIL_ADDRESS>Simon Miles<EMAIL_ADDRESS>Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil  TU
Dortmund, Dortmund, Germany  University of Warwick, Coventry, United Kingdom
University of Essex, Colchester, United Kingdom  King’s College London,
London, United Kingdom

###### Abstract

Reputation is crucial to enabling human or software agents to select among
alternative providers. Although several effective reputation assessment
methods exist, they typically distil reputation into a numerical
representation, with no accompanying explanation of the rationale behind the
assessment. Such explanations would allow users or clients to make a richer
assessment of providers, and tailor selection according to their preferences
and current context. In this paper, we propose an approach to explain the
rationale behind assessments from quantitative reputation models, by
generating arguments that are combined to form explanations. Our approach
adapts, extends and combines existing approaches for explaining decisions made
using multi-attribute decision models in the context of reputation. We present
example argument templates, and describe how to select their parameters using
explanation algorithms. Our proposal was evaluated by means of a user study,
which followed an existing protocol. Our results give evidence that although
explanations present a subset of the information of trust scores, they are
sufficient to equally evaluate providers recommended based on their trust
score. Moreover, when explanation arguments reveal implicit model information,
they are less persuasive than scores.

###### keywords:

Reputation , Trust , Explanation , Arguments , User study

††journal: International Journal of Human-Computer Studies

##  1 Introduction

In environments where many parties offer comparable services or products,
customers need to be able to choose between the options available. Automated
support for this has been studied extensively in the areas of recommender
systems [1] and reputation assessment [2]. In particular, reputation
assessment allows the calculation of reputation scores so that the past
performance of service providers can be compared. These scores can then be
used to determine which provider to select, as they characterise providers
according to the factors of interest to the client. Various reputation models
[3, 4, 5, 6, 7] have been shown to be effective through empirical evaluation,
but do not provide the transparency needed to understand why one provider has
a better reputation than another. As the complexity of reputation models
increases, this understanding is becoming harder to achieve. Access to the
reasons that underlie reputation assessment would allow users to judge whether
the resulting reputation scores reflect their actual interests in the current
context, and allow providers to identify the aspects they must improve.
Explanations have been exploited to improve user system acceptance in expert
systems and recommender systems [8], but have not been explored in the context
where automated interactions occur, such as in multi-agent systems, or
instantiated for reputation assessment methods.

Our goal is to improve, from the user perspective, the _transparency_ of
reputation models, which are in general purely quantitative. Reputation scores
are helpful to assess and rank providers but, with explanations of such
scores, users would be able to evaluate whether they agree with them. As a
consequence, users can make more _effective_ choices when taking reputation
into account. We propose an approach to explain the rationale behind the
scores generated by reputation assessment models. These are abstracted into a
generic reputation model, which we refer to as the _multi-term reputation
model_ (MTRM). This is not a new reputation model, but rather is a generalised
model in which we can express existing reputation assessment methods, upon
which explanations can be built. Our approach generates arguments about the
reasons behind reputation scores by leveraging explanation approaches proposed
in the context of multi-attribute utility theory [9, 10], and combines the
arguments into explanations. Explanations are produced based on information
that can be obtained from an instance of MTRM. Moreover, this generic
reputation model can be customised, leading to an instantiation of a specific
underlying existing reputation model, and model-specific arguments can then be
generated. In order to illustrate this process, we show customisations for the
FIRE [4] and TRAVOS [5] reputation models.

Despite the fact that users have generally been taken out of the loop in
evaluations of work on trust and reputation for multi-agent systems, a study
involving real people is essential for validating our approach. Therefore, in
order to evaluate our generated explanations, we conducted a user study, which
provides evidence of their usefulness. The study involve 30 participants and
followed the protocol proposed by Bilgic and Mooney [11]. As result, we
observed that, in order to assess providers, our explanations is as efficient
as having detailed information about trust scores of providers, that is, with
less information (and possibly more confidentially) participants were able to
assess providers. Furthermore, our explanation arguments are less persuasive
than scores when they reveal implicit model information. In our study,
arguments were presented to participants in a textual form, generated using
example templates of how to transform our explanation arguments into a user-
understandable form. This choice caused participants, however, to prefer trust
scores, which were presented in a table, over textual explanations.

In summary, our key contribution is an approach to explain quantitative
reputation models, focusing on FIRE and TRAVOS as illustrative reputation
models. Specifically, we (i) propose a method to generate explanations of
assessments from quantitative reputation models, (ii) show how to leverage
existing approaches for explaining decisions made using multi-attribute
decision models in the context of reputation, and (iii) evaluate such
explanations through a user study.

We describe background research and related work in Section 2. The multi-term
reputation model (MTRM) is introduced in Section 3, followed by a description
of our explanation approach in Section 4. The user study performed to evaluate
our approach is presented in Section 5. Finally, we present our conclusions in
Section 6.

##  2 Background and Related Work

Two main research areas are associated with our work, namely, explanations for
recommender and decision support systems, and trust and reputation assessment
methods. There is much work that has been done in the former, but not
addressing our particular context. We give an overview of explanation
approaches and introduce those that are adopted in our work in Section 2.1.
Trust and reputation have also been widely investigated and, as a result, many
reputation models have been proposed. Our approach aims to be generic, in the
sense that it can be used with any reputation model. We instantiate it for
illustration using two existing reputation models, FIRE [4] and TRAVOS [5,
12], as described in Section 2.2.

###  2.1 Explanation Generation

Over recent years, there has been an increasing interest in explanations for
recommender and decision support systems [8, 13, 14]. Explanations in such
systems have been investigated, as was the case with expert systems [15],
because explanations can promote many benefits, including increased user trust
and more effective decisions [8], which are fundamental to user acceptance of
these systems.

Different studies have been performed in the context of explanations. Many
types of explanations given for recommender systems were compared in user
studies [13, 16]. Herlocker et al. [13] concluded that showing rates from
neighbours in the context of collaborative filtering (using histograms)
contributes to the acceptance of the recommendation. However, Bilgic and
Mooney [11] observed that this kind of explanation persuades users to accept
recommendations rather than helping them to make better choices. Indeed,
explanations can be given with different purposes [8]. As Bilgic and Mooney
argue, persuasion explanations cause users to overestimate the quality of an
option and make inaccurate choices and, consequently, their confidence in the
system rapidly deteriorates. Our interest is thus in _effective_ explanations
[8], which assist users to make better decisions by helping them to evaluate
the quality of options according to their own preferences. There are some
studies with people that give foundation to this kind of explanation [17, 18],
with the proposal of patterns and guidelines, which state that attributes
presented in explanations must be tailored to the user, as has been confirmed
by a previous user study [19].

There are three main approaches that propose algorithms that select attributes
to be part of effective explanations [20, 9, 10]. Such approaches use multi-
attribute decision models as input, which makes them inadequate to be used as
is with reputation models. However, they can be used in a complementary way in
our work, by being adapted to be used in our context.

The oldest approach, proposed by Klein and Shortliffe [20], is empirically
motivated but lacks proper evaluation, while Labreuche’s approach [9]
addresses a limitation of this method—a formal justification of the selected
arguments. Labreuche [9] proposed an approach for selecting and generating
arguments for the family of multi-attribute decision models parameterised by
weights assigned to the criteria, such as the expected utility model and the
weighted majority model. The explanations generated are of four different
types, generated using different kinds of argumentation reasoning, called
_anchors_ (_all_ , _not on average_ , _invert_ and _remaining case_). Anchors
identify changes in a weight vector $v$ that yields an inversion of the
prescription made by the decision model, leading to why one option is
preferred to another. Two strategies for the modification of the weights are
considered: (i) the replacement of $v$ by some reference weights
$w^{\mathcal{F}}$, indicating that an option is preferred to another because
it is better for the most important attributes, but not on average, and (ii) a
permutation of the weights $v$ among the criteria (associated with a branch-
and-bound algorithm), indicating that the preferred option is better for the
most important attributes and worse for the least important attributes. A
trivial anchor addresses the case of domination (the case where an option has
at least one advantage with respect to another, and no disadvantage), and
another last anchor covers the remaining cases.

An explanation generation technique was proposed by Nunes et al. [10], which
is founded on a study of how people justify choices [18]. The technique is
composed of a set of algorithms that select attributes to be used as part of
explanations that follow different explanation patterns, such as _critical
attribute_ , _cut-off value_ , _decisive criteria_ and _trade-off resolution_.
While Klein and Shortliffe’s approach selects outlier attributes and Labreuche
analyses weight changes, Nunes et al. consider a set of attributes as decisive
when they are the minimum set of attributes (in the sense of $\subset$) needed
to make an option worse than another. If this set consists of all cons of an
option, then a second set of attributes is selected: the minimum set of
attributes that are pros that must not be taken into account to enable the
existence of a decisive criteria.

We have used adapted parts of these two introduced approaches [9, 10] in the
work described in this paper, and further details of these parts are provided
when we describe our explanation approach.

Argumentation frameworks have also been adopted for the purpose of empowering
quantitative decision tools with inference mechanisms and respective
explanation capabilities—e.g. argumentation-enriched recommender systems have
been proposed for recommending music [21], movies [22, 23], web content [24],
and learning objects [25]. In many such approaches, Defeasible Logic
Programming (DeLP) [26] is employed either instead, or on top of an existing
quantitative technique in order to provide a qualitative perspective, where
conclusions/suggestions are reasoned in terms of arguments for and against
them. In particular, DeLP models (potentially inconsistent and contradictory)
knowledge about the domain, in terms of facts and a set of strict and
defeasible inference rules. An argument for a particular conclusion/suggestion
is then derived by applying backward chaining on these facts and rules.
Arguments can be attacked by other arguments (e.g. those proposing opposite
conclusions), and the attacks among arguments can be resolved via associating
arguments with probabilities/preferences.

The knowledge (facts and rules) upon which the reasoning of such argumentation
frameworks is based is typically pre-determined, and is derived directly from
user preference declarations, and added on top of the (sub-)results of the
quantitative measure. Our explanation approach focuses on providing a finer-
grained analysis of the reasoning behind the quantitative measure (rather than
substituting it or building on top of it), and can be seen as a dynamic
generator of knowledge to then be used by such argumentation frameworks.

###  2.2 Reputation Models

Trust and reputation enable agents to minimise the inherent uncertainty when
self-interested individuals or organisations interact [27]. Trust can be
viewed as an assessment of the likelihood that an individual or organisation
will fulfil its commitments [28]. Reputation complements trust, and can be
seen as a public perception of trustworthiness [29]. Several computational
models of trust and reputation exist, which can be broadly categorised into
those that are based on credentials and those based on experience and
observation of past behaviour—see [27, 29, 2, 30] for comprehensive reviews.
Credential-based approaches use policies to express when, for what, and how to
determine trust based on certificates, keys, or digital signatures, etc.
Although such methods are effective for managing access rights and
permissions, they do not support more general reasoning about interactions,
and therefore in this paper we focus instead on experience based approaches.

Several experience based approaches use a combination of direct and indirect
experience to derive a numerical or probabilistic assessment of reputation
[31]. ReGreT [32, 3] assesses reputation on three aspects: (i) an individual
dimension from direct experience, (ii) a social dimension using knowledge of
others’ experiences and the social structure, and (iii) an ontological
dimension that accounts for the different aspects that inform reputation (e.g.
delivery, price, and quality). FIRE [4] builds on ReGreT through the addition
of role-based trust, and certified reputation based on third-party references
[4]. TRAVOS [5] takes a probabilistic approach to assessing trust, estimating
the expected value of success of future interactions using a beta probability
distribution.

The use of a binary variable (success or failure) to model outcomes is a
limitation of TRAVOS and alternative approaches have been proposed. For
example, BLADE [6] models agents and advisor evaluation functions as dynamic
random variables using Dirichlet distributions, enabling progressive learning
of probabilistic models through Bayesian techniques. To cope with noisy
advisors, HABIT [7] creates a Bayesian network to support reasoning about
reputation. However, HABIT assumes that the distribution of an agent’s
behaviour is static, an assumption not made by other approaches. Other
reputation systems apply machine learning in assessing reputation, typically
in assessing stereotypical reputation [33, 34].

Although these methods rely on different aggregations/distributions, they have
been used for the same purpose of estimating the reputation of agents with
which an agent wants to interact, relying on evaluations made based on
previous interactions (either by direct experience or with peers) over time.
In this paper, we adopt FIRE and TRAVOS as examples to illustrate our
approach, and describe their operation in more detail below. We focus on FIRE
and TRAVOS due to their simplicity and low computational overheads, compared
to approaches such as BLADE and HABIT, because the focus of this paper is on
explanation generation providing a rationale for reputation assessment, rather
than on any particular reputation assessment method itself. We selected two
methods to demonstrate the generality of our approach and the value of
customisations made to particular methods.

###  2.3 The FIRE Reputation Model

FIRE combines four types of reputation and trust: interaction trust from
direct experience ($I$), witness reputation from third party reports ($W$),
role-based trust ($R$), and certified reputation based on third-party
references ($Cr$) [4]. Reputation is assessed in FIRE from rating tuples,
$(a,b,t,i,v)$, where $a$ and $b$ are agents that participated in interaction
$i$ such that $a$ gave $b$ a rating value of $v\in[-1,+1]$ for the term $t$
(e.g. reliability, quality, timeliness). A rating of $+1$ is absolutely
positive, $-1$ is absolutely negative, and $0$ is neutral. In FIRE, each agent
has a history of size $H$ and stores the last $H$ ratings it has given in its
local database. FIRE gives more weight to recent interactions using a rating
weight function, $\omega_{K}$, for each trust or reputation component
$K\in\\{I,W,R,Cr\\}$.

The component trust or reputation $a$ has in $b$ for term $t$ is the weighted
mean of ratings,

$\mathcal{T}_{K}(a,b,t)=\frac{\sum_{r_{i}\in\mathcal{R}_{K}(a,b,t)}\omega_{K}(r_{i})\cdot
v_{i}}{\sum_{r_{i}\in\mathcal{R}_{K}(a,b,t)}\omega_{K}(r_{i})}$ (1)

where $\mathcal{R}_{K}(a,b,t)$ is the set of ratings stored by $a$ regarding
$b$ for component $K$ with respect to term $t$, and $v_{i}$ is the value of
rating $r_{i}$. Interaction trust, $\mathcal{T}_{I}(a,b,t)$ is calculated from
the interaction records that the assessing agent $a$ has in their database,
$\mathcal{R}_{I}(a,b,t)$. Specifically, the ratings of records matching
$(a,b,t,\\_,\\_)$ are aggregated using Equation 1, where $b$ is the agent
being assessed, $t$ is the term of interest, and “$\\_$” matches any value,
and:

$\omega_{I}(r_{i})=e^{-\frac{\Delta\tau(r_{i})}{\lambda}}$ (2)

Here, $\omega_{I}(r_{i})$ is the weight for rating $r_{i}$ and
$\Delta\tau(r_{i})$ is the time since rating $r_{i}$ was recorded.

Witness and certified reputation are similarly calculated, using this
aggregation over different sets of interaction ratings. For witness reputation
the assessing agent, $a$, uses a acquaintances to provide their ratings of $b$
for term $t$, i.e. ratings of the form $(\\_,b,t,\\_,\\_)$. If the
acquaintance has no relevant experience, they will pass on the request to
their own acquaintances. To assess certified reputation, the assessed agent,
$b$, provides a set of ratings that they have previously been given by other
agents. The weighting used in calculating witness and certified reputation is
$\omega_{W}(r_{i})=\omega_{C}r(r_{i})=\omega_{I}(r_{i})$.

Role-based trust uses ratings assigned to rules describing agent
relationships, e.g. if they are part of the same organisation, or there is a
provider consumer relationship. Rules have the form
$(role_{a},role_{b},t,e,v)$, representing if two agents $a$ and $b$ take roles
$role_{a}$ and $role_{b}$, then $b$ is expected with a likelihood of
$e\in[0,1]$ to have performance of $v$ for term $t$ in an interaction with
$a$. To calculate role-based trust, rules in the assessing agent’s database
that match $\mathcal{R}_{R}(a,b,t)$ are aggregated using Equation 1, with
$\omega_{R}(r_{i})=e_{i}$.

The composite term trust, $\mathcal{T}(a,b,t)$, in an agent with respect to a
given term $t$ is calculated as a weighted mean of the component sources:

$\mathcal{T}(a,b,t)=\frac{\sum_{K\in\\{I,W,R,Cr\\}}\omega_{K}\cdot\mathcal{T}_{K}(a,b,t)}{\sum_{K\in\\{I,W,R,Cr\\}}\omega_{K}}$
(3)

where $\omega_{I}$, $\omega_{W}$, $\omega_{R}$ and $\omega_{Cr}$ are
parameters that determine the importance of each component,
$\omega_{K}=\omega_{K}\cdot\rho_{K}(a,b,t)$, and the reliability of the
reputation value for component $K$ is $\rho_{K}(a,b,t)$. The reliability of a
reputation value is determined by a combination of the rating reliability and
rating deviation reliability (details of the calculations can be found in
[4]).

###  2.4 The TRAVOS Reputation System

TRAVOS is based on the Beta Reputation System [35] and extends it to ignore
reputation assessments from unreliable witnesses [5, 36, 12]. TRAVOS uses
interaction trust and witness reputation, computed using rating tuples similar
to those used in FIRE. Whereas in FIRE the rating value is a real number,
ratings in TRAVOS are binary, $v\in\\{0,1\\}$, where $0$ is a negative rating
and $1$ is positive. The component trust value agent $a$ has in agent $b$ with
respect to term $t$, is the expected value of a beta probability density
function,

$\mathcal{T}_{K}(a,b,t)=\frac{\alpha_{K}(a,b,t)}{\alpha_{K}(a,b,t)+\beta_{K}(a,b,t)},$
(4)

where $\alpha_{K}(a,b,t)$ is $1$ plus the number of relevant positive ratings
and $\beta_{K}(a,b,t)$ is $1$ plus the number of relevant negative ratings,

$\displaystyle\alpha_{K}(a,b,t)$
$\displaystyle=1+|\\{r_{i}\in\mathcal{R}_{K}(a,b,t)|v_{i}=1\\}|,$ (5)
$\displaystyle\beta_{K}(a,b,t)$
$\displaystyle=1+|\\{r_{i}\in\mathcal{R}_{K}(a,b,t)|v_{i}=0\\}|.$

The beta probability density function can also be used to compute a confidence
in the trust value, defined by the proportion of the distribution that lies in
a range centred around the expected value,

$\mathcal{\rho}_{K}(a,b,t)=\frac{\int_{\mathcal{T}_{K}(a,b,t)-\epsilon}^{\mathcal{T}_{K}(a,b,t)+\epsilon}X^{\alpha_{K}(a,b,t)-1}(1-X)^{\beta_{K}(a,b,t)-1}dX}{\int_{0}^{1}U^{\alpha_{K}(a,b,t)-1}(1-U)^{\beta_{K}(a,b,t)-1}dU},$
(6)

where $\epsilon$ is a user defined parameter to define the range considered.

As with FIRE, an assessing agent computes interaction trust from the set of
ratings, $\mathcal{R}_{I}(a,b,t)$, in its database that match
$(a,b,t,\\_,\\_)$. The interaction trust is then $\mathcal{T}_{I}(a,b,t)$,
which has an associated confidence, $\mathcal{\rho}_{I}(a,b,t)$. If
$\mathcal{\rho}_{I}(a,b,t)$ is below a threshold set by the user, witnesses
are asked for ratings of agent $b$ for term $t$, which are used to compute the
witness reputation.

Witnesses, $w\in W$, provide opinions in the form of the number of positive,
$\alpha_{W}(w,b,t)$ and the number of negative ratings, $\beta_{W}(w,b,t)$,
that they have given $b$. Before the overall reputation is calculated, the
witness opinions are discounted based on their perceived accuracy to limit
their effect on the composite reputation score. TRAVOS stores previous ratings
provided by witnesses in _observation_ tuples, $(a,w,b,t,i,o,v)$, where, $w$
is a witness that provided evaluator $a$ with a set of ratings about provider
$b$, which formed a beta probability density distribution whose expected value
determined the raw opinion value of $o$. After processing this witness opinion
and selecting $b$ to interact with, $a$ gave $b$ a rating value of $v$ in
interaction $i$.

On receipt of a new opinion from a witness, $w$, an evaluator, $a$, queries
their observation database for records where the opinion, $o$, provided by $w$
for term $t$ was similar. Two opinions are said to be similar if their
expected values are close (i.e. they both lie in the same discrete interval).
The coherence of the opinion provided, $o$, and the rating for the subsequent
interaction, $v$, then determines the reliability of the new opinion provided
by the witness. Given this reliability, the opinion is discounted and combined
along with the interaction trust by summing the $\alpha$ and $\beta$
parameters,

$\displaystyle\alpha(a,b,t)$ $\displaystyle=\alpha_{I}(a,b,t)+\sum_{w\in
W}\bar{\alpha}_{W}(w,b,t)$ (7) $\displaystyle\beta(a,b,t)$
$\displaystyle=\beta_{I}(a,b,t)+\sum_{w\in W}\bar{\beta}_{W}(w,b,t),$

where $\bar{\alpha}_{W}(w,b,t)$ and $\bar{\beta}_{W}(w,b,t)$ are the
discounted opinion parameters provided by witness $w$ regarding agent $b$ for
term $t$. The composite term trust in agent $b$ for term $t$ is then,

$\mathcal{T}(a,b,t)=\frac{\alpha(a,b,t)}{\alpha(a,b,t)+\beta(a,b,t)}.$ (8)

For full details on the calculation behind discounting see [12].

##  3 Multi-Term Reputation Model

In the previous section, we gave an overview of two different reputation
models, namely FIRE and TRAVOS. In order to provide a model-independent
explanation approach, we must first specify a common model specification that
generalises different reputation models. This generalised model, which we
refer to as _multi-term reputation model_ (MTRM), can be specialised by the
addition of the specific components of a particular reputation model. Note
this MTRM is not a new reputation model, but a model that captures concepts
present in _any_ reputation model. Therefore, explanations provided based on
this model are applicable to any reputation model. Concepts that are usual,
e.g. recency, but not used in all reputation models can be added in MTRM
extensions. We next introduce the MTRM concepts.

All reputation models consider a way for an agent to assess how an interaction
with another agent occurred. In FIRE, for example, agents associate a rating
with those they interact with in $[-1,+1]$, while in TRAVOS agents only record
success or failure, i.e. ratings are in $\\{0,1\\}$. These ratings are then
communicated to others who require additional information to inform their
decisions. In our model, we consider that an agent is associated with a set of
trust ratings

$r_{i}=\langle a,b,t,K,v\rangle$ (9)

where $a$ is a source agent, $b$ is target agent, $t$ is a term, $K$ is a
reputation type, and $v$ is a rating value. A particular reputation model may
add additional parameters, e.g. interaction as in FIRE. Trust ratings are
associated with reputation types, or components, according to the component of
the model that generates them. Each model incorporates a particular set of
reputation types, $K_{Set}$. TRAVOS only includes interaction ($I$) and
witness ($W$) reputation types, while FIRE supplements those with role-based
($R$) and certified ($Cr$) reputation. The set of ratings associated with a
particular reputation type is $\mathcal{R}_{K}(a,b,t)$.

These ratings are used to calculate a trust value $\mathcal{T}_{K}(a,b,t)$,
which combines trust ratings in a single real value. In case of FIRE, as
introduced in Section 2.3, the trust value is a weighted mean of ratings,
considering a recency function $\omega_{\lambda}(r_{i})$, while TRAVOS uses a
probabilistic model. If a trust value is associated with a reputation type
$K$, it means that it is derived from ratings only associated with $K$.

Trust values associated with different reputation types must be combined to
form a single value. In MTRM, as its name indicates, we consider that agents
can assess others with respect to different terms $t\in T$, such as cost,
quality and timeliness. The component trust values can be combined to form the
term trust $\mathcal{T}(a,b,t)$. We do not assume that the term trust is
calculated using a specific method such as a weighted mean or sum, but rather
we assume that the term trust can be decomposed into weights $\omega_{K}$ and
trust values $\mathcal{T}_{K}(a,b,t)$, associated with different reputation
types. This is straightforward in FIRE, given that FIRE calculates trust as a
weighted mean of weighted means. However, TRAVOS does not calculate a
composite trust value from interaction and witness trusts in this way, instead
combining ratings from witnesses, after adjustment for reliability and
relevance, to act as parameters of a beta probability distribution whose
expected value determines the composite trust value. Consequently, we use the
TRAVOS model to compute the term trust from ratings, and then decompose this
term trust into two trust values, one associated with direct interaction
trust, and another with witness trust.

TRAVOS computes an overall trust value, which in our case is the term trust,
by combining the direct interaction trust and witness opinions, after
adjusting them for perceived accuracy. The combination proceeds by summing the
$\alpha$ and $\beta$ parameters of the beta probability density functions, as
in Equation 7. In FIRE, the trust component weights are determined by user
preferences, while in TRAVOS, we define them as the proportion of the final
beta probability density function that the component parameters account for.
For instance, the interaction trust weight is,

$\omega_{I}(a,b,t)=\frac{\alpha_{I}(a,b,t)+\beta_{I}(a,b,t)}{\alpha_{I}(a,b,t)+\beta_{I}(a,b,t)+\sum_{w\in
W}\bar{\alpha}_{W}(w,b,t)+\bar{\beta}_{W}(w,b,t)}$ (10)

and witness reputation weight is $\omega_{W}(a,b,t)=1-\omega_{I}(a,b,t)$.

Finally, existing reputation models either do not consider terms (e.g. TRAVOS)
or often do not specify how to combine values for different terms into a
single trust score (as is the case with FIRE). Therefore, inspired by multi-
attribute utility theory [37], we consider weights that establish a trade-off
relationship among terms, and view term trust as a utility value. The overall
trust score is then a weighted mean of term trusts, where the weights are
agents’ preferences for terms.

$\mathcal{T}(a,b)=\frac{\sum_{t\in
T}\omega_{t}\cdot\mathcal{T}(a,b,t)}{\sum_{t\in T}\omega_{t}}$ (11)

where the parameters $\omega_{t}$ correspond to $a$’s preferences regarding
the relative importance of terms, and $T$ is the set of all terms.

Note that in order for reputation models to be abstracted to our MTRM, they
should either use a weighted sum approach, like FIRE, or be decomposable into
such an approach, like TRAVOS.

As result, our MTRM is able to capture data such as that presented in Table 1.
In this table, we show a set of illustrative trust values from the perspective
of an agent $A$ with respect to four other agents ($B$, $C$, $D$ and $E$),
considering three different terms—Quality (Q), Timeliness (T) and Cost (Ct).
For example, $\mathcal{T}_{I}(A,D,T)=0.95$. These trust values are
combinations of ratings by, for example, a recency function. Similarly, there
are trust values that come from witnesses, which are shown in the columns
labelled with “Witness Trust Values” in Table 1, for instance
$\mathcal{T}_{W}(A,C,Q)=0.40$.

| Interaction | Witness |  |
---|---|---|---|---
| Trust Values | Trust Values | Term Trusts | Trust Score
| Q | T | Ct | Q | T | Ct | Q | T | Ct |
B | 0.75 | 0.55 | 0.40 | 0.95 | 0.70 | 0.30 | 0.80 | 0.59 | 0.38 | 0.64
C | 0.10 | 0.20 | 0.15 | 0.40 | 0.15 | 0.15 | 0.18 | 0.19 | 0.15 | 0.17
D | 0.50 | 0.95 | 0.10 | 0.60 | 0.80 | 0.10 | 0.53 | 0.91 | 0.10 | 0.58
E | 0.10 | 0.20 | 0.40 | 0.90 | 1.00 | 0.95 | 0.30 | 0.40 | 0.54 | 0.38
Weight | 0.45 | 0.35 | 0.20 |  |  |  |  |  |  |
Table 1: Running Example: Agents and Scores.

The term trust, in this case, combines interaction and witness trust values.
Assume that agent $A$ uses the following weights: (i) interaction weight:
$\omega_{I}=0.75$; and (ii) witness weight: $\omega_{W}=0.25$. As result, for
example, we have the quality trust with respect to $B$ would be
$\mathcal{T}(A,B,Q)=0.75\times 0.75+0.25\times 0.95=0.80$.

Similarly, term trusts are combined using weights, which are shown in the last
row of Table 1, for terms resulting in the overall trust score, shown in the
last column in Table 1—for instance, $\mathcal{T}(A,C)=0.17$. Based on these
calculations, it can be seen that the agent with the best trust score is agent
$B$. Although there is a mathematical explanation that leads to this, it is
hard to extract intuitive arguments that justify why $B$ is the most
trustworthy agent for agent $A$. This is done by our explanation approach,
which is presented in the following section.

##  4 Explaining Reputation Assessments

Now that we have a common reputation model, we can specify a method for
producing explanations. An explanation justifies why a particular agent (e.g.
a service provider) has a better reputation, i.e. the overall trust score,
than another from the perspective of a given agent (e.g. a client). Our
explanations are produced by generating a set of arguments, which give the key
aspects that distinguish the two agents being compared, being all arguments
needed to understand which agent is better. Arguments are instantiated with
parameters selected using specified algorithms. We first present arguments
that can be part of an explanation, and then show how to use these arguments
to produce an explanation.

Our method not only produces arguments for our common trust model, MTRM, but
also considers the specific details of different reputation models. Therefore
we have generic arguments, generated based on MTRM, which are supplemented
with model-specific arguments. We show as examples of the latter specific
arguments for both FIRE and TRAVOS, which are used as illustrative reputation
assessment models in this paper.

###  4.1 Explanation Arguments

We first look at the possible classes of reasons why a provider may have a
better reputation than another. Such classes are associated with the different
components that are part of MTRM. Each class has a corresponding argument type
that can be used as part of an explanation. The generation of arguments here
is similar to the identification of decisive criteria to explain choices made
using multi-attribute decision models. We select, adapt and combine the
algorithms of Labreuche [9] and Nunes et al. [10] to produce our arguments. As
described earlier, an agent’s overall trust score is a weighted mean of term
trust values, and each of these can be decomposed into trust values for
different reputation types. Correspondingly, our argument types are split into
three groups, namely decisive terms, decisive reputation types, and reputation
model-specific arguments, as described below. For simplicity, but without loss
of generality, we assume that ratings are in $[0,1]$, given that the
approaches we leverage use this range. FIRE and TRAVOS ratings can be easily
mapped to this range.

####  4.1.1 Argument: Decisive Terms

The reputation of a provider for a client is a balance among trust values for
terms, corresponding to aspects of an interaction or service such as quality
or timeliness. Some terms may be irrelevant with respect to why one provider
is more trusted than another, either because they have low weight for the
client or because the differences between term trust values for providers are
small. To explain why provider $b$ has a better overall trust score than
provider $b^{\prime}$ for an agent $a$, we must identify the _decisive_ terms
$\mathcal{D}(a,b,b^{\prime})=\langle P,C\rangle$ that lead to this conclusion,
where $P$ and $C$ are sets of terms that are the decisive _pros_ and _cons_ of
$b$ with respect to $b^{\prime}$, respectively. For example, if
$P=\\{quality,cost\\}$ and $C=\\{timeliness\\}$, we can derive an argument of
the form “ _$b$ is more trusted than $b^{\prime}$ because it has higher trust
for quality and cost, even though $b^{\prime}$ has higher trust for
timeliness_”.

A trivial case is that of _domination_ , when $b$ has advantages compared to
$b^{\prime}$ with respect to some terms and no disadvantages with respect to
the remaining terms. According to Labreuche, important terms are those that
have weights higher than the reference weight, which is defined as the weight
that makes all terms equality important (used in the _not on average_ anchor,
$\Psi_{NOA}$). That is, if there are $n$ terms, the reference weight is
$\omega^{\mathcal{A}}=1/n$. We need to adapt this to take into account the
trust values for terms. Considering the difference between term trust for a
term $t$ for providers $b$ and $b^{\prime}$,
$\Delta_{t}=|\mathcal{T}(a,b,t)-\mathcal{T}(a,b^{\prime},t)|$, we can say that
the reference value difference is $\Delta^{\mathcal{A}}=\frac{\sum_{t\in
T}\Delta_{t}}{|T|}$, where $T$ is the set of terms. Thus,
$\Delta^{\mathcal{A}}$ is the average of the differences between trust values
for all terms. Given the reference weight and reference value difference, the
reference weighted value difference is
$\omega^{\mathcal{A}}\cdot\Delta^{\mathcal{A}}$. Decisive terms in the case of
domination are consequently those whose weighted value difference is higher
than the reference weighted value difference, i.e.

$\mathcal{D}_{Dom}(a,b,b^{\prime})=\langle\\{t\in
T|\omega_{t}\cdot\Delta_{t}>\omega^{\mathcal{A}}\cdot\Delta^{\mathcal{A}}\\},\emptyset\rangle$
(12)

Informally, decisive pros are terms that have: (i) above average weight and
value, (ii) very high weight, or (iii) very high value. In this context “
_very high_ ” means that even though $\Delta_{t}<\Delta^{\mathcal{A}}$,
$\omega_{t}$ is high enough to cause
$\omega_{t}\cdot\Delta_{t}>\omega^{\mathcal{A}}\cdot\Delta^{\mathcal{A}}$, and
the same reasoning is applied to $\Delta_{t}$. As provider $b$ dominates
$b^{\prime}$, there are no cons in this case.

In order to illustrate the domination case, we use the example introduced in
the previous section, considering the values presented in Table 1. By
analysing the term trusts of agents $B$ and $C$, it is possible to see that
$B$ dominates $C$, because $B$ has higher trust values for all terms. In order
to identify the decisive terms, we first calculate the reference value
difference, which is

$\Delta^{\mathcal{A}}=\frac{|0.80-0.18|+|0.59-0.19|+|0.38-0.15|}{3}=\frac{0.63+0.40+0.23}{3}=0.42$

As $\omega^{\mathcal{A}}=0.33$,
$\omega^{\mathcal{A}}\cdot\Delta^{\mathcal{A}}=0.14$. Calculating the weighted
differences for quality, timeliness and costs, we obtain 0.28, 0.14 and 0.05,
respectively. As only the first two are above the reference weighted value
difference111The reference weighted value is, more precisely, 0.139., they are
the decisive terms. An explanation argument, in this case, would be as
follows.

Example 1: $B$ has a better reputation than $C$, because it is better in all
aspects that you consider in your preferences, mainly with respect to
timeliness, and quality.

When dominance is not the case, we could apply either Labreuche’s anchors [9]
or the patterns of Nunes et al. [10] to select decisive criteria. As the
number of terms $|T|$ may be high and Labreuche’s approach may have
performance issues [10], we use the latter, which is briefly explained as
follows. We first define $T_{+}=\\{t\in
T|\mathcal{T}(a,b,t)>\mathcal{T}(a,b^{\prime},t)\\}$ and $T_{-}=\\{t\in
T|\mathcal{T}(a,b,t)<\mathcal{T}(a,b^{\prime},t)\\}$, which are the sets of
all pros and cons of $b$ with respect to $b^{\prime}$, respectively. Using
these patterns, the decisive criteria is
$\mathcal{D}_{DC}(a,b,b^{\prime})=\langle T_{+}^{*},T_{-}^{*}\rangle$, such
that $T_{+}^{*}\subseteq T_{+}$, $T_{-}^{*}\subseteq T_{-}$, and

$\sum_{t\in T_{+}^{*}}\omega_{t}\cdot\Delta_{t}>\sum_{t\in
T_{-}/T_{-}^{*}}\omega_{t}\cdot\Delta_{t}$ (13)

$T_{+}^{*}$ and $T_{-}^{*}$ are both minimal in the sense of $\subseteq$. When
$T_{-}^{*}=\emptyset$, it is a decisive criteria pattern, otherwise it is a
trade-off resolution pattern.

In order to better understand the selection of decisive terms when there is no
dominance, we use our running example. Consider agents $B$ and $D$. According
to the trust value, the former has two pros, namely quality (weighted
difference is $0.12$) and cost (weighted difference is $0.06$), while the
latter has only timeliness (weighted difference is $0.11$) as pros. In order
to justify why $D$ is less trustworthy than $B$, considering only quality
would be enough, because its weighted difference is already higher than the
weighted difference of timeliness (its con). Therefore, quality is $B$’s
decisive criteria with respect to $D$. This is illustrated in the argument
below.

Example 2: $B$ has a better reputation than $D$, mainly due to quality.

####  4.1.2 Argument: Decisive Reputation Types

The key argument produced to explain why provider $b$ is more trusted than
provider $b^{\prime}$ is the set of terms that are the decisive pros of $b$
with respect to $b^{\prime}$, and occasionally the decisive cons of
$b^{\prime}$. Term trusts are derived from ratings of different kinds of
sources, referred to as reputation types, $K$, being a composition of trust
values considering different sources. Therefore, we can again leverage
algorithms used for multi-attribute decision models, to refine the
explanation.

When $b$ dominates $b^{\prime}$ for a term $t$, i.e. there exists $K$ in the
set of reputation types such that
$\mathcal{T}_{K}(a,b,t)>\mathcal{T}_{K}(a,b^{\prime},t)$ and there is no
$K^{\prime}$ such that
$\mathcal{T}_{K^{\prime}}(a,b,t)<\mathcal{T}_{K^{\prime}}(a,b^{\prime},t)$,
then stating that $t$ is a decisive term is sufficient, and no additional
argument is needed. In other cases, it is relevant to add new arguments to the
explanation. For example, assume that $b$ has a higher trust score than
$b^{\prime}$ considering a component $I$ (for interaction trust), $b^{\prime}$
has a higher trust score than $b$ considering $W$ (for witness trust), and
$\omega_{I}\gg\omega_{W}$ ($I$ is more important than $W$). In this case, it
is helpful to state the argument “ _even though $b^{\prime}$ has higher
ratings from third party reports, $b$ has higher ratings from direct
experience, which is more important_.”

Our pairwise analysis of weights and values is done with Labreuche’s _invert_
anchor, $\Psi_{IVT}$. Although this anchor had performance issues in a
previously performed experiment with human participants [10], this occurred
where there was a high number of attributes, which in our case corresponds to
reputation types. We assume there is a small number of reputation types (e.g.
there are four in FIRE and two in TRAVOS) and so performance is not an issue
here. The argument given for explaining trust values considering reputation
types is a permutation $\pi(a,b,b^{\prime},t)=\\{(K,K^{\prime})\in S^{2}\\}$,
where $S\subseteq K_{Set}$, such that
$\mathcal{T}(a,b,t)<_{\pi(a,b,b^{\prime},t)}\mathcal{T}(a,b^{\prime},t)$. The
operator $<_{\pi(a,b,b^{\prime},t)}$ compares two term trusts applying the
permutation $\pi(a,b,b^{\prime},t)$ to reputation type weights. Consequently,
$\pi(a,b,b^{\prime},t)$ gives a set of pairwise changes in weights, which
causes the term trust of $b^{\prime}$ to be higher than that of $b$. Labreuche
provides a branch-and-bound algorithm for the determination of this kind of
explanation [9], which for brevity is not reproduced here. Given that there
are limited possible permutations in our case, algorithmic efficiency is not
critical.

Considering our running example, we have a case of decisive reputation types
considering agents $B$ and $E$ with respect to the timeliness term. The trust
value of agent $B$ is better considering interaction ratings ($0.55>0.20$),
while the trust value of agent $E$ is better considering witnesses ratings
($1.00>0.70$). If the weights given to the interaction and witnesses ratings
were inverted, $E$ would have a higher term trust than $B$—timeliness trust
would be 0.66 for $B$ and $0.80$ for $E$, instead of $0.59$ and $0.40$,
respectively. We present below a textual argument that gives this explanation.

Example 3: Considering timeliness, even though $E$ has higher reputation with
respect to witness reputation, which is less important, $B$ has has higher
reputation with respect to own interaction, which is more important.

####  4.1.3 Reputation Model-specific Arguments

The way that trust and reputation values are derived from ratings is different
for each reputation model. As a consequence, it is possible to provide further
arguments other than our generic arguments if we take model particularities
into account. In this case, model-specific arguments can be generated and used
to supplement the generic arguments. In addition, arguments can be added not
only to explain trust scores, but to give further details about trust values
and term trusts. Here, to illustrate these possibilities, we present two
model-specific arguments: a FIRE-specific argument associated with trust
values and a TRAVOS-specific argument to further explain term trusts.

##### FIRE-specific Argument: Recency

The trust value for a particular reputation type in FIRE is calculated through
a weighted mean of available ratings $v_{i}$. Weights can be used to assign
more importance to particular ratings, specifically more recent ratings have a
higher weight. The ratings are thus scaled using a rating recency factor
$\lambda$, as introduced before. The recency factor may play a key role both
in the overall trust score and in the trust value for particular $t$ and $K$.
The overall trust score of a provider uses $\omega_{\lambda}(r_{i})$ to
combine available ratings $\mathcal{R}_{K}(a,b,t)$, associated with a
particular $a$, $b$ and $t$. In this case, we can also consider a reference
rating weight function $\omega_{\lambda_{K}}^{\mathcal{A}}$, which is the
average weight, i.e.

$\omega_{\lambda_{K}}^{\mathcal{A}}=\frac{1}{|\mathcal{R}_{K}(a,b,t)|}$ (14)

Given this reference function, two situations might occur. First, the order
derived from the overall trust score of providers $b$ and $b^{\prime}$,
calculated taking into account recency, conflicts with the order derived from
the overall trust score calculated using $\omega_{\lambda_{K}}^{\mathcal{A}}$.
That is, we have $\mathcal{T}(a,b)>\mathcal{T}(a,b^{\prime})$ and
$\mathcal{T}^{\mathcal{A}}(a,b)<\mathcal{T}^{\mathcal{A}}(a,b^{\prime})$,
where $\mathcal{T}^{\mathcal{A}}(a,b)$ is the overall trust calculated using
$\omega_{\lambda_{K}}^{\mathcal{A}}$. Second, even though this situation may
not occur, there may still be cases where
$\mathcal{T}_{K}(a,b,t)>\mathcal{T}_{K}(a,b^{\prime},t)$ and
$\mathcal{T}^{\mathcal{A}}_{K}(a,b,t)<\mathcal{T}^{\mathcal{A}}_{K}(a,b^{\prime},t)$,
for a particular $K$ and $t$. In the first scenario, we add an argument
$\mathcal{F}(a,b,b^{\prime})$ to the explanation explaining that “ _although
on average $b^{\prime}$ has higher ratings than $b$, recently $b$ has been
receiving higher ratings than $b^{\prime}$, which are more valuable_”. In the
second case, we must add a finer-grained argument
$\mathcal{F}(a,b,b^{\prime},t,K)$, for specific $K$ and $t$: “ _although on
average $b^{\prime}$ has higher ratings for t than $b$, considering K,
recently $b$ has been receiving higher ratings than $b^{\prime}$, which are
more valuable_”.

##### TRAVOS-specific Argument: Low Confidence

FIRE uses weights of reputation types to express their importance for a
particular assessor agent, and they remain fixed unless an assessor explicitly
changes them. Therefore, a set of interaction and witness ratings does not
influence the weights of reputation types to calculate a trust score. TRAVOS,
on the other hand, evaluates how useful interaction ratings are, before taking
witness ratings into account. If an assessor does not have enough confidence
into its own ratings, i.e. the confidence is below a given threshold, then
witness ratings are used, otherwise it will rely on its own ratings.

Therefore, it is important to know whether the trust score is based solely on
interaction ratings or on both interaction and witness ratings. If
$\mathcal{\rho}_{I}(a,b,t)$ (interaction confidence) is below a threshold set
by the assessor, for any of the providers being assessed, it means that
witness ratings are being taken into account to consider $b$ better than
$b^{\prime}$, i.e. $\mathcal{T}_{W}(a,b,t)>\mathcal{T}_{W}(a,b^{\prime},t)$.
When this is the case, we add an argument $\mathcal{C}(a,b,b^{\prime},t)$ to
the explanation, which can be written in natural language in the following
form: “ _although you have had limited previous interactions with either $b$
or $b^{\prime}$ with respect to $t$, the former is considered better than the
latter by witnesses_”.

###  4.2 Explanation Generation

Above, we introduced the different arguments that can be used to form an
explanation to justify why a provider $b$ has a higher trust score than a
provider $b^{\prime}$. In this section, we show how to generate such an
explanation. We first identify our coarse-grained argument to justify trust
scores. This argument is composed of decisive terms, which has the form
$\mathcal{D}(a,b,b^{\prime})$ and gives the decisive pros and cons justifying
the overall trust scores. When $b$ dominates $b^{\prime}$, i.e. exists $t\in
T$ such that $\mathcal{T}(a,b,t)>\mathcal{T}(a,b^{\prime},t)$ and there is no
$t^{\prime}\in T$ such that
$\mathcal{T}(a,b,t^{\prime})<\mathcal{T}(a,b^{\prime},t^{\prime})$, the
decisive criteria are given by $\mathcal{D}_{Dom}(a,b,b^{\prime})$, otherwise
they are given by $\mathcal{D}_{DC}(a,b,b^{\prime})$.

Once we know the decisive criteria that justify trust scores, we can provide
fine-grained arguments that provide further understanding, considering
decisive terms $t\in P$. First, we search for those that have a trust score
associated with decisive reputation types. This is given by
$\pi(a,b,b^{\prime},t)$, which is a permutation of weights given for the
different reputation types, indicating that the weights involved in that
permutation are decisive, because if they were assigned in a different way, we
would have $\mathcal{T}(a,b,t)<\mathcal{T}(a,b^{\prime},t)$. Second, we add
model-specific arguments. For example, in the case of FIRE, the arguments
$\mathcal{F}(a,b,b^{\prime})$ and $\mathcal{F}(a,b,b^{\prime},t,K)$ are added
when the selected recency weight function is the cause for making the trust
value of $b$ higher than that of $b^{\prime}$, i.e. if equal weights were
given to all ratings, this would not have been the case. While in the case of
TRAVOS, the argument $\mathcal{C}(a,b,b^{\prime},t)$ is added when interaction
ratings are limited, and thus the opinions of witnesses are taken into
account.

1

Input: $a$: an agent; $b,b^{\prime}$: service providers

Output: $\phi$: explanation with a set of arguments

2 if  _dominates (b,b’)_ then

3 $\phi\leftarrow\\{\mathcal{D}_{Dom}(a,b,b^{\prime})\\}$;

4

5 else

6 $\phi\leftarrow\\{\mathcal{D}_{DC}(a,b,b^{\prime})\\}$;

7

8 addSpecificArguments ($\phi$);

9 foreach  _$t\in P$_ do

10 if  _$\exists\pi(a,b,b^{\prime},t)$ such that
$\mathcal{T}(a,b,t)<_{\pi}\mathcal{T}(a,b^{\prime},t)$_ then

11 $\phi\leftarrow\phi\cup\\{\pi(a,b,b^{\prime},t)\\}$;

12 addSpecificTermTrustArguments
($\phi,t,\mathcal{T}(a,b,t),\mathcal{T}(a,b,t^{\prime})$);

13 foreach  _$K\in K_{Set}$_ do

14 addSpecificTrustValueArguments
($\phi,t,K,\mathcal{T}_{K}(a,b,t),\mathcal{T}_{K}(a,b,t^{\prime})$);

15

16

17 return _$\phi$_ ;

Algorithm 1 Expl($a,b,b^{\prime}$)

This method is presented in Algorithm 1, which generates an explanation
$Expl(a,b,b^{\prime})$ to justify why provider $b$ has a higher trust score
than provider $b^{\prime}$, for agent $a$. An explanation is thus a set of
arguments of the types introduced above. Note that in Algorithm 1, fine-
grained arguments are generated only for terms that are decisive pros.
However, arguments may be also generated for decisive cons, if one wants to
provide further details about the trust score. No fine-grained arguments are
generated for the remaining terms, since they are not decisive. In addition,
Algorithm 1 calls functions that add additional arguments to the explanations.
These functions must be specified for specific trust models. For example, in
the case of FIRE we can add recency arguments to explain the trust score as a
whole and particular trust values, as shown in Algorithms 2 and 3. Similarly,
for TRAVOS we can add arguments to explain term trust, as shown in Algorithm
4.

1

Input: $\phi$: explanation

Output: $\phi$: explanation with added arguments

2

3if  _$\mathcal{T}(a,b) >\mathcal{T}(a,b^{\prime})$ and
$\mathcal{T}^{\mathcal{A}}(a,b)<\mathcal{T}^{\mathcal{A}}(a,b^{\prime})$_ then

4 $\phi\leftarrow\phi\cup\\{\mathcal{F}(a,b,b^{\prime})\\}$;

5

6 return _$\phi$_ ;

Algorithm 2 FIRE: addSpecificArguments

1

Input: $\phi$: explanation; $t$: term; $K$: reputation type
$\mathcal{T}_{K}(a,b,t)$, $\mathcal{T}_{K}(a,b^{\prime},t)$: trust values

Output: $\phi$: explanation with added arguments

2

3if  _$\mathcal{T}_{K}(a,b,t) >\mathcal{T}_{K}(a,b^{\prime},t)$ and
$\mathcal{T}^{\mathcal{A}}_{K}(a,b,t)<\mathcal{T}^{\mathcal{A}}_{K}(a,b^{\prime},t)$_
then

4 $\phi\leftarrow\phi\cup\\{\mathcal{F}(a,b,b^{\prime},t,K)\\}$;

5 return _$\phi$_ ;

Algorithm 3 FIRE: addSpecificTrustValueArguments

1

Input: $\phi$: explanation; $t$: term; $\mathcal{\rho}_{I}(a,b,t)$,
$\mathcal{\rho}_{I}(a,b^{\prime},t)$: confidence; $\mathcal{T}_{W}(a,b,t)$,
$\mathcal{T}_{W}(a,b^{\prime},t)$: trust values

Output: $\phi$: explanation with added arguments

2

3if  _( $\mathcal{\rho}_{I}(a,b,t)<\epsilon$ or
$\mathcal{\rho}_{I}(a,b^{\prime},t)<\epsilon$) and
$\mathcal{T}_{W}(a,b,t)>\mathcal{T}_{W}(a,b^{\prime},t)$._ then

4 $\phi\leftarrow\phi\cup\\{\mathcal{C}(a,b,b^{\prime},t)\\}$;

5 return _$\phi$_ ;

Algorithm 4 TRAVOS: addSpecificTermTrustArguments

Finally, we now show how an explanation $Expl(a,b,b^{\prime})$, which is a set
of arguments, can be translated to human-readable form. For illustration, we
adopt a textual form. Parts shown in brackets are optional, and thus may not
appear in all explanations. Note that two of the optional arguments are FIRE-
specific and one is TRAVOS-specific. In addition, optional arguments may be
added more than once, depending on the number of arguments that are part of
the explanation.

_Provider $b$ has a better reputation than  Provider $b^{\prime}$ mainly due
to  list of pros in $P$ [, even though  Provider $b^{\prime}$ provides better
list of cons in $C$ ]C≠∅._

_[In addition, Provider $b^{\prime}$ has, on average, higher ratings than
Provider $b$ , but  Provider $b$ has been recently receiving higher ratings
than  Provider $b^{\prime}$ , which are more
valuable.]${}_{\mathcal{F}(a,b,b^{\prime})}$_

_[Considering Term $t$ , even though  Provider $b^{\prime}$ has a higher trust
value considering  Reputation Type $K$ , which is less important,  Provider
$b$ has a higher trust value considering  Reputation Type $K^{\prime}$ , which
is more important.]${}_{\forall(K,K^{\prime})\in\pi(a,b,b^{\prime},t)}$_

_[Moreover, although you have had limited previous interactions with either
Provider $b$ or  Provider $b^{\prime}$ with respect to  Term $t$ , the former
is considered better than the latter by
witnesses.]${}_{\forall\mathcal{C}(a,b,b^{\prime},t)}$_

_[Moreover, Provider $b^{\prime}$ has, on average, higher ratings for  Term
$t$ than $b$, considering  Reputation Type $K$ , but  Provider $b$ has been
recently receiving higher ratings than  Provider $b^{\prime}$ , which are more
valuable.]${}_{\forall\mathcal{F}(a,b,b^{\prime},t,K)}$_

##  5 User Study

In this section, we therefore present a user study conducted to evaluate our
proposed explanation approach.

###  5.1 Goal and Research Questions

Reputation assessment models are often used in multiagent systems to allow
autonomous agents (which can be humans) to identify in which agents they can
trust to interact with. Our explanations can be used as a means for agents to
exchange information regarding the reputation of other agents, without the
need for exposing the reputation model details or detailed scores. However, as
our explanations reveal less information than components of trust scores, we
must evaluate if they are helpful for agents or users to better choose another
agent (which can be, e.g. a service provider) to interact with. More
specifically, we aim to answer the following research questions.

  1. 1.

Are our explanations more effective in helping users to understand reputation-
based recommendations than quantitative scores alone?

  2. 2.

How do users perceive the usefulness of our explanations?

In order to answer these questions, we present our explanations to users using
our example explanation templates. Our hypothesis is that users are better
able to understand the rationale behind recommendations when they receive
explanations instead of only quantitative information (i.e. reputation
scores). Our first research question is aligned with this hypothesis. However,
given that the effectiveness of such explanations may be different to how
users perceive their usefulness, the second research question aims to explore
this relationship.

###  5.2 Procedure

Our user study followed an adaptation of the protocol previously adopted to
conduct user studies that involve the evaluation of explanations in
recommender systems [11, 16]. The steps of this protocol are the following
[11]: (1) get sample ratings from the user; (2) compute a recommendation $r$;
(3) for each explanation system, present $r$ to the user with $e$’s
explanation and ask the user to rate $r$; and (4) ask the user to try $r$ and
then rate it again. In the remainder of this section we present the steps we
followed to conduct the user study.

##### Construction of Provider Model

Our study involves participants rating and receiving recommendations of
service providers based on reputation models. In order to have a set of
providers to be part of the study, we create a set of simulated providers.
Providers are described with a model that specifies the probabilities of
transaction outcomes, e.g. considering a provider of delivery services, an
outcome is the number of days taken to deliver a package. Outcomes are
associated with terms, e.g. the outcome of delivering a package is associated
with the term timeliness.

##### Participant Data and Preference Elicitation

Participants initiate the study by providing data about themselves and
preferences for different terms. Additionally, they provide preferences for
reputation types, required by the FIRE model.

##### Collection of Sample Ratings

From each participant, we collect 15 sample ratings in the following way: (i)
randomly select a provider, (ii) simulate an interaction by generating
outcomes based on the provider model, and (iii) present the result of the
interaction to the participant and ask them to rate the provider with respect
to each term. We present an example of an interaction outcome in Figure 1a.
Note that providers may be selected more than once, and likely have different
outcomes in each interaction. Each set of ratings is associated with a round,
which is interpreted as a timestamp for FIRE and a round for TRAVOS. These
sample ratings are used to build both the FIRE and TRAVOS models for each
participant. Participants provide ratings with a value between 0 and 1 (or not
applicable). For FIRE, this value is used as is, and for TRAVOS we used a
threshold of 0.5 to distinguish between successful and unsuccessful
interactions. Moreover, TRAVOS requires a confidence threshold, which was set
to 0.2. We selected a low threshold given that participants have few repeated
experiences with the same provider, causing confidence to be usually low. In
this way, we balance situations where witness opinions are used or not.

(a) Sample Ratings: Generated Outcome.

(b) Full Provider Information.

Figure 1: Screenshots of the Web Application (1).

##### Explanation Evaluation

We randomly select three providers from the set of providers and rank them
using their computed reputation scores (step 2 of the protocol), which are
based on the reputation model, ratings (from the participant and peers) and
preferences. We randomly select the model to be used and which explanatory
information is provided to users: (i) FIRE with scores alone, (ii) FIRE with
explanation arguments alone, (iii) TRAVOS with scores alone, or (iv) TRAVOS
with explanation arguments alone. Examples of explanation arguments and scores
are shown in Figures 2a and 2b, respectively. Note that participants are not
aware that there are two underlying reputation models driving the
recommendations. Then, we show to participants the provider ranking, together
with the selected explanatory information (step 3 of the protocol), and ask
them to answer in a 7-point Likert scale whether they agree with the
statement: _Considering the information provided above, I would order the
presented providers in the same way that they were ordered, according to my
preferences._ Next, we show participants the same ranking together with the
full provider model (i.e. the probabilities of the outcomes), such as
presented in Figure 1b, so that they know all possible details about this
provider, and ask them again the same question (step 4 of the protocol). Based
on these answers we measure how the scores given for the first question
(scores or explanation arguments) differ from the scores given for the
provider model. With full information of providers’ probabilities,
participants know exactly what to expect by interacting with providers;
however, this complete information is usually unknown. Therefore, the
participant score with respect to full information is used as a baseline: the
closer the participant score for explanation arguments or reputation scores,
the better. This is therefore the metric we collect to evaluate the
effectiveness of explanatory information, in the form of absolute difference
between the two answers, referred to as _score difference_. This step is
repeated 10 times for each participant.

(a) Explanation Arguments.

(b) Explanation Scores.

Figure 2: Screenshots of the Web Application (2).

##### Perceived Effectiveness Questionnaire

To collect information regarding the _perceived_ value of the provided
explanations, we ask participants to evaluate (in a 7-point Likert scale) the
two forms of describing providers (with textual explanations and with
reputation scores) with respect to (i) transparency: _I understand why the
providers were ranked in the presented way through the explanations_ and (ii)
trust: _I feel that these explanations are trustworthy_. In addition, we also
ask an open-ended question to participants, in which participants have to
explain their preference for scores or explanation arguments.

###  5.3 Target Domain and Application Support

To execute the procedure described above, we implemented a web application to
support the study, from which screenshots are presented in Figure 1 and 2. We
selected _delivery services_ as the domain, given that it is suitable for our
scenario, because: (i) people in general have used this kind of service at
least once and, if not, they are aware of how it works and its possible
outcomes, and (ii) participants do not need to _concretely_ experience such
services to be able to evaluate them, i.e. the domain can be simulated.

Service providers are modelled with probabilities associated with different
outcomes, which are listed in Table 2. For example, providers are associated
with a constant value that indicates the maximum days they take to deliver a
package. They are also associated with a variable representing the average
number of days that it takes to deliver packages and the standard deviation.
Therefore, to simulate the number of days taken we used randomisation with a
normal distribution defined by these parameters.

Table 2: Provider Model and Terms. Outcome |  Domain Values |  Outcome |  Term
---|---|---|---
|  |  Model |
Number of days to deliver |  Integer $>0$ |  Normal distribution $(\mu,\sigma)$ |  Timeliness
Maximum days to deliver |  Integer $>0$ |  Constant |
Price |  Double $>0$ |  Constant |  Price
Parcel Condition |  Perfect Conditions Damaged Package Damaged Product Lost |  Probabilities |  Quality of Service
Customer Service |  Easy to contact and problem solved Easy to contact but problem unresolved Difficult to contact but problem solved Difficult to contact and problem unresolved |  Probabilities |  Customer Support
- |  - |  - |  Reliability

Participants evaluate providers with respect to each term presented in the
rightmost column of Table 2. These terms are associated with the outcome that
we believe that the participant would take into account to rate a term. Note
that reliability is not associated with any outcome, since we assume that this
is related to repeated experiences that the participant has with the same
provider.

We modelled 10 providers, each being associated with two sets of model
parameters. We use the first set of parameters to collect the first half of
the set of sample ratings, and the second set of parameters to collect the
remaining samples. In this way, we simulate change in the providers’
behaviour, and allow for the fact that the ratings provided can change over
time.

###  5.4 Participants and Preferences

Our study participants were selected using _convenience sampling_. Graduate
and undergraduate students of a Brazilian Computer Science program were
invited to participate as volunteers. Data was collected in two separate time
slots, and participants that participated within the same time slot were
considered peers, in order to compute witness trust. In total, our study
involved _30 participants_ , such that 9 participated in the first time slot
and 21 participated in the second. We detail characteristics of the
participants in Table 3.

Table 3: Characteristics of Participants (N = 30). Age | 16–25 years | 26–35 years
---|---|---
| 23 (77%) | 7 (23%)
Gender | Male | Female
| 29 (97%) | 1 (3%)
Course | Undergraduate | Graduate
Level | 23 (77%) | 7 (23%)

In addition to collecting participant characteristics, we also asked them to
provide their preferences with respect to reputation types and terms.
Descriptive information was provided to allow them to understand the required
information. In Table 4, we present the preferences provided by participants.
Note that in this study we consider only interaction and witness reputation
types.

Table 4: Participant Preferences for Reputation Types and Terms. Reputation Type/Term | M | SD
---|---|---
Interaction | 0.635 | 0.11
Witness | 0.365 | 0.11
Customer Support | 0.138 | 0.08
Price | 0.202 | 0.09
Quality of Service | 0.237 | 0.05
Reliability | 0.242 | 0.07
Timeliness | 0.181 | 0.07

###  5.5 Results and Analysis

We now present our study results, analysing first objective effectiveness and
then perceived effectiveness. Hereafter explanation arguments and trust scores
are referred to as _arguments_ and _scores_ , respectively.

####  5.5.1 Objective Effectiveness

The metric used to analyse objective effectiveness is the score difference
between that given to explanatory and full information. Our aim is to evaluate
collected scores in a single group but, because we had two separate
participant groups (in order to obtain witness ratings), we first investigated
whether results obtained are similar for both groups. We ran a Mann-Whitney’s
U test to compare group responses and, as expected, their is no significant
difference between the scores provided by the two groups (U = 9436, p-value =
0.98).

Considering participant scores, we obtained the results presented in the
second (mean, M) and third (standard deviation, SD) columns of Table 5.
Results are split into four groups (rows), according to the reputation model
used (FIRE or TRAVOS) and the provided explanatory information (arguments or
scores). Score differences for the four groups are also shown in Figure 3a, in
a box plot, which presents the mean, median and variance of values. As can be
seen, results diverge between FIRE and TRAVOS: while scores performed better
considering FIRE, arguments outperformed scores considering TRAVOS. Despite
these differences, a Kruskal-Wallis test revealed that the differences are not
significant ($\chi^{2}$ = 13.7, p = 0.94). Although arguments and scores
achieved similar results, this is already evidence of the effectiveness of our
arguments. Arguments refer to a small portion of the information revealed by
scores (it selects only decisive criteria, and provides further information
only with respect to them). Therefore, we state our first finding as follows.

Finding 1: Information that is not present in arguments can indeed be
discarded, because it is not helpful to better evaluate providers, as
otherwise using scores would have had a better performance.

Note that scores and arguments were presented separately in our study in order
to understand the effectiveness of arguments in isolation, but we are not
suggesting that this should be the case in real applications. We assume that
they can be presented together, so that they can complement each other.

Table 5: Summary of Score Differences. Reputation Model/ | Overall | Model-specific Arguments | Agreement with the Model
---|---|---|---
Explanatory |  | With | Without | Agree | Disagree | Neutral
Information | M | SD | M | SD | M | SD | M | SD | M | SD | M | SD
FIRE/Arguments | 1.81 | 1.53 | 1.76 | 1.52 | 1.88 | 1.57 | 1.00 | 1.00 | 3.15 | 1.33 | 1.00 | 0.82
FIRE/Scores | 1.75 | 1.58 | - | - | - | - | 0.87 | 1.03 | 2.93 | 1.51 | 1.75 | 0.96
TRAVOS/Arguments | 1.80 | 1.67 | 1.83 | 1.82 | 1.75 | 1.40 | 1.18 | 1.10 | 2.47 | 1.97 | 2.50 | 0.71
TRAVOS/Scores | 1.97 | 1.79 | - | - | - | - | 1.05 | 1.07 | 3.42 | 1.82 | 2.50 | 0.71

(a) Overall Score Differences.

(b) Score Differences by Agreement with the Model.

Figure 3: Overview of Scores Differences.

This initial analysis of our results showed that the differences among the
four groups are not statistically significant. However, a deeper analysis
allowed us to reveal interesting findings, which explain the contradicting
results between FIRE and TRAVOS. First, we analysed whether the difference
between the values obtained for FIRE and TRAVOS was due to the model quality,
i.e., one model produces rankings that better match users’ opinions. Model
quality is evaluated by checking whether the ranking produced by the
reputation model matches the ranking that the users would produce, when they
are aware of the full provider information. Consequently, in order to evaluate
model quality, we used only the scores given by participants considering the
full provider information. As shown in Table 6, rankings using trust scores
calculated by FIRE and TRAVOS received similar ratings. Moreover, roughly, the
same amount of participants agreed with the rankings produced by models.
Indeed, Mann-Whitney’s U test indicates that the difference between the scores
obtained with full information is not significant (U = 11482, p-value = 0.7).

Second, we investigated whether the model-specific arguments played a key role
in our results. However, this was also not the case. In our results 61.96% of
the provided explanations contained model-specific arguments (61.36% for FIRE,
and 62.67% for TRAVOS). In columns 6–7 of Table 5, we detail the score
differences between explanations provided with and without model-specific
argument. We ran a Kruskal-Wallis test that showed that the differences are
not significant ($\chi^{2}$ = 0.22, p = 0.97).

Table 6: Quality of Reputation Assessment Models. Reputation Model | M | SD | Agree | Disagree | Neutral
---|---|---|---|---|---
FIRE | 4.48 | 2.00 | 56.25% | 38.75% | 5.00%
TRAVOS | 4.37 | 2.17 | 55.71% | 41.43% | 2.86%

We then analysed whether the agreement with model influenced the results.
Scores were split into three groups: (i) _agree_ : when participants provided
a score greater than 4 considering the full provider information, (ii)
_disagree_ : when participants provided a score lower than 4, and (iii)
_neutral_ : when participants provided a score equals to 4. Results are
detailed in the last six columns of Table 5. They are also shown in Figure 3b,
where the x-axis has labels with three letters: the first stands for Agree,
Disagree, or Neutral, the second stands for FIRE or TRAVOS, and the third
stands for Arguments or Scores. We observed that participants, in general,
tend to agree with the ranking based on explanatory information, because this
is only the information they have, which is in accordance with the ranking
(the ranking is derived from scores). Consequently, changes occur more often
from agree to disagree than from disagree to agree, i.e., participants more
often agree with the ranking considering explanatory information, and then
change their opinion to disagree when they learn the full provider
information. A Kruskal Wallis test revealed a significant difference among the
groups ($\chi^{2}$ = 97.7, p $<$ 0.01). A post-hoc test using Mann-Whitney
tests with Bonferroni correction showed significant differences between the
agree groups AFA, AFS and all disagree groups, and the agree groups ATA, ATS
and the disagree groups (DFA, DFA, DTS). There is no significant difference
between ATA and ATS, and DTA. This supports our second main finding.

Finding 2: Except arguments provided for TRAVOS (i.e. TRAVOS/Arguments), all
combinations of reputation model with explanatory information (i.e.
FIRE/Arguments, FIRE/Scores and TRAVOS/Scores) _persuades_ participants to
agree with the ranking.

Our explanation approach thus managed to be not (or less) persuasive for one
of the models, and this is a positive aspect of our approach. This result
becomes evident in Figure 4, in which we show the distribution of how
participants evaluated the ranking based on explanatory information (divisions
in columns shown in x-axis) according to how they actually evaluate it, i.e.
based on full provider information (y-axis). For example, from all cases in
which participants evaluated FIRE/Arguments and they agreed with the model
based on full provider information, in 90% they agreed with the ranking based
on explanatory information, in 6% they disagreed with the model (when in fact
they agree), and in 4% they were neutral with the model. In most of the cases,
participants agreed with the ranking based on explanatory information. Only
with TRAVOS/Arguments, did they manage to more often perceive based on
arguments that they actually disagree with the ranking (35% of the cases).

Figure 4: Distribution of evaluation based on explanatory information by
agreement with the model.

We further investigated why this occurred, because this result is unexpected
given that: (i) the reputation models are equally good, and (ii) explanation
arguments are similar in FIRE and TRAVOS except for model-specific arguments,
but explanations with model-specific arguments are not better than those
without. A key difference between FIRE and TRAVOS is that FIRE uses weights
for reputation types that are given and TRAVOS calculates them, based on
similarity between witnesses and interaction ratings. Consequently, even
though the _decisive reputation types_ argument is used for both FIRE and
TRAVOS, it reveals information of different nature. While in FIRE it just
acknowledges participants that their preference for reputation types played a
key role in the recommender, in TRAVOS it reveals a detail of the model that
may be not in accordance with the participant preferences, e.g., the model
gave importance to witnesses opinions while the participant believes that such
opinions are not that important. Therefore, our hypothesis that explains this
result leads to our third finding.

Finding 3: Arguments that reveal implicit model information, which is the
result of a calculation or an assumption regarding user preferences, are
essential for users to better understand the rationale behind reputation
assessments and use such information to make better decisions.

####  5.5.2 Perceived Effectiveness

In addition to the evaluation of the objective effectiveness of our approach,
we also analysed how participants perceive the explanations. Results with
respect to transparency and trust in our explanations, presented in Figure 5,
show that participants prefer scores instead of textual explanations. A
Wilcoxon Signed-ranks test indicated that the difference between scores (M =
6.17; SD = 1.02) and arguments (M = 4.83; SD = 1.53) with respect to
transparency is statistically different (W = 25.5; p $<$ 0.01), and the
difference between scores (M = 5.73; SD = 1.53) and arguments (M = 3.70; SD =
1.39) with respect to transparency is also statistically different (W = 58.5;
p $<$ 0.01). This result was expected given that our explanation arguments,
when translated to a textual form, requires the user to read a possibly large
set of sentences, and a previous study [10] showed that this may cause users
to dislike it. Based on this, we state our fourth and last finding.

Finding 4: It is important to identify graphical forms of presenting the
information captured by our explanation arguments.

Figure 5: Questionnaire Scores: Transparency and Trust.

Note that although participants indicated that scores were more transparent
than explanation arguments, as shown, they are similarly effective and
arguments are less persuasive under certain circumstances. Moreover, even
though lengthy explanations are criticised by participants, they do not impact
on effectiveness or efficiency. This is shown in Figure fig:explanationLength,
which shows the lack of correlation between the explanation length and score
differences (effectiveness) and time to analyse them (efficiency). The results
of our subjective analysis, however, provide evidence of the need for better
means of translating our explanation arguments into a human-readable
presentation format.

(a) Explanation Length vs. Score Difference.

(b) Explanation Length vs. Time.

Figure 6: Analysis of the Impact of Explanation Length.

Interestingly, some participants did not realise that the textual-based
explanations were explaining the scores, and believed that the arguments were
trying to convince them to agree with the ranking. When justifying their
transparency and trust scores, five participants reported that textual
explanations can persuade them and scores cannot, mainly because they can see
the exact difference between scores, but our results show that this is not the
case. In fact, as discussed in the related work section, a study concluded
that showing ratings from neighbours can persuade users to accept
recommendations [13], so this previous study and ours converge to the same
direction. Four participants highlighted benefits of our arguments, such as
providing meaning to small quantitative differences or analysing recency. One
of the participants made the following comment: _“The explanations with scores
can [be] ambiguous sometimes, specially when scores differ on small amounts
e.g How much is 0.002 of reliability? However, textual explanations not only
remove that ambiguity, but also make certain aspects of the ordering explicit,
such as your personal weights, and recent scoring being more important than
overall, for example.”_ Finally, two participants reported that although they
prefer scores, the textual explanations provide complementary information,
which is the main aim in our case.

##  6 Conclusion

In this paper, we have presented an approach to generating explanations of why
providers of services were considered to have more or less reputation than
other providers. This involved abstracting existing reputation assessment
models into a generalised model that we used as a base to produce
explanations. In our work, we leveraged existing explanation approaches (for
multi-attribute decision models) to determining decisive arguments when
choosing between options, to account for the different values that are
weighted in reputation assessment, such as the weighting between a client’s
own past experience and the information it has gathered from its peers. We
presented a model by which concise arguments could be extracted from the
reputation assessment process and combined into explanations. Explanation
arguments were evaluated with a user study. We concluded that, although
explanations present a subset of the information of trust scores, they are
sufficient to equally evaluate providers recommended based on their trust
score. Moreover, when explanation arguments reveal implicit model information,
they are less persuasive than scores. Despite these positive aspects of our
explanations, given that they are presented in a textual form, which requires
more cognitive effort to analyse, participants showed preference for analysing
scores instead of reading sentences.

For illustration, we have considered in this paper the FIRE and TRAVOS
reputation models. However, our approach is unchanged if an alternative
reputation model is adopted, as long as it can be mapped to our generalised
multi-term reputation model. We do not assume a particular representation of
behaviour or source of information, nor require a particular method of
assessing reputation from available sources. We identify the overall decisive
criteria for a provider being preferred to another, and subsequently identify
the corresponding model-specific arguments that support the assessment. The
process of identifying the criteria and generating explanations is unchanged,
but the details of the criteria may be different, e.g. criteria for ReGreT
[32, 3] might consider trust ascribed to the groups to which agents belong,
while for HABIT [7] the criteria would refer to probabilistic estimations of
future behaviour.

We currently focused on using and evaluating our approach with human users.
However, automated negotiation environments can also potentially benefit from
our explanations. For example, when automated providers are selected (or not
selected) by clients, they can ask for explanations to help them improve their
services. In addition, explanations can be used by clients to improve their
choices by refining their preferences. Clients may also use explanations to
change their network neighbours. If a client observes that it always chooses
providers because they are better rated considering its own experience, even
though ratings given by peers are higher, the client may understand that its
ratings diverge from its peers, and possibly look for new neighbours.
Moreover, explanations may be used to share information among clients. For
instance, a client concerned with privacy issues can state to other clients
which provider is better than another using an explanation as a rationale,
without revealing their preferences and ratings. All these different
directions will be explored in our future work.

## Acknowledgements

This work was part funded by the UK Engineering and Physical Sciences Research
Council as part of the Justified Assessments of Service Provider Reputation
project, ref. EP/M012654/1 and EP/M012662/1. Ingrid Nunes thanks for research
grants CNPq ref. 303232/2015-3, CAPES ref. 7619-15-4, and Alexander von
Humboldt, ref. BRA 1184533 HFSTCAPES-P.

## References

## References

  * [1] F. Ricci, L. Rokach, B. Shapira, P. B. Kantor, Recommender Systems Handbook, 1st Edition, Springer-Verlag New York, Inc., New York, NY, USA, 2010.
  * [2] S. D. Ramchurn, T. D. Huynh, N. R. Jennings, Trust in multi-agent systems, The Knowledge Engineering Review 19 (1) (2004) 1–25.
  * [3] J. Sabater, Evaluating the ReGreT system, Applied Artificial Intelligence 18 (9-10) (2004) 797–813.
  * [4] T. D. Huynh, N. R. Jennings, N. R. Shadbolt, An integrated trust and reputation model for open multi-agent systems, Journal of Autonomous Agents and Multi-Agent Systems 13 (2) (2006) 119–154.
  * [5] W. T. L. Teacy, J. Patel, N. R. Jennings, M. Luck, Coping with inaccurate reputation sources: Experimental analysis of a probabilistic trust model, in: Proceedings of the 4th International Conference on Autonomous Agents and Multiagent Systems, 2005, pp. 997–1004.
  * [6] K. Regan, P. Poupart, R. Cohen, Bayesian reputation modeling in e-marketplaces sensitive to subjectivity, deception and change, in: Proceedings of the 21st National Conference on Artificial Intelligence, 2006.
  * [7] W. T. L. Teacy, M. Luck, A. Rogers, N. R. Jennings, An efficient and versatile approach to trust and reputation using hierarchical bayesian modelling, Artificial Intelligence 193 (2012) 149–185.
  * [8] N. Tintarev, J. Masthoff, Designing and evaluating explanations for recommender systems, in: Recommender Systems Handbook, Springer US, 2011, pp. 479–510.
  * [9] C. Labreuche, A general framework for explaining the results of a multi-attribute preference model, Artif. Intell. 175 (7-8) (2011) 1410–1448.  doi:10.1016/j.artint.2010.11.008.
  * [10] I. Nunes, S. Miles, M. Luck, S. Barbosa, C. Lucena, Pattern-based explanation for automated decisions., in: Proceedings of the 21th Eureopean Conference on Artificial Intelligence, ECAI’2014, 2014, pp. 669–674.
  * [11] M. Bilgic, R. Mooney, Explaining recommendations: Satisfaction vs. promotion, in: Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces, 2005\.
URL http://www.cs.iit.edu/~ml/pdfs/bilgic-iui05-wkshp.pdf

  * [12] W. T. L. Teacy, J. Patel, N. R. Jennings, M. Luck, TRAVOS: Trust and reputation in the context of inaccurate information sources, Journal of Autonomous Agents and Multi-Agent Systems 12 (2006) 183–198.
  * [13] J. L. Herlocker, J. A. Konstan, J. Riedl, Explaining collaborative filtering recommendations, in: CSCW ’00, ACM, New York, NY, USA, 2000, pp. 241–250.
  * [14] G. Carenini, J. D. Moore, Generating and evaluating evaluative arguments, Artif. Intell. 170 (2006) 925–952.
  * [15] L. R. Ye, P. E. Johnson, The impact of explanation facilities on user acceptance of expert systems advice, MIS Q. 19 (1995) 157–172.  doi:http://dx.doi.org/10.2307/249686.
  * [16] F. Gedikli, D. Jannach, M. Ge, How should I explain? a comparison of different explanation types for recommender systems, Int. J. Hum.-Comput. Stud. 72 (4) (2014) 367–382.  doi:10.1016/j.ijhcs.2013.12.007.
  * [17] N. Tintarev, J. Masthoff, Effective explanations of recommendations: user-centered design, in: Proc. of the 2007 ACM conference on Recommender systems, RecSys ’07, USA, 2007, pp. 153–156.
  * [18] I. Nunes, S. Miles, M. Luck, C. J. P. de Lucena, Investigating explanations to justify choice, in: Proceedings of the 20th International Conference on User Modeling, Adaptation, and Personalization, UMAP’12, Springer-Verlag, Berlin, Heidelberg, 2012, pp. 212–224.
  * [19] G. Carenini, J. D. Moore, An empirical study of the influence of user tailoring on evaluative argument effectiveness, in: Proceedings of the 17th international joint conference on Artificial intelligence, IJCAI’01, USA, 2001, pp. 1307–1312.
  * [20] D. A. Klein, E. H. Shortliffe, A framework for explaining decision-theoretic advice, Artif. Intell. 67 (1994) 201–243.  doi:10.1016/0004-3702(94)90053-1.
  * [21] C. Briguez, M. Budán, C. Deagustini, A. Maguitman, M. Capobianco, G. Simari, Towards an argument-based music recommender system, in: Frontiers in Artificial Intelligence and Applications, Vol. 245, 2012, pp. 83–90.
  * [22] J. A. Recio-García, L. Quijano, B. Díaz-Agudo, Including social factors in an argumentative model for group decision support systems, Decision Support Systems 56 (2013) 48–55.
  * [23] C. E. Briguez, M. C. Budán, C. A. Deagustini, A. G. Maguitman, M. Capobianco, G. R. Simari, Argument-based mixed recommenders and their application to movie suggestion, Expert Systems with Applications 41 (14) (2014) 6467 – 6482.
  * [24] C. Chesñevar, A. G. Maguitman, M. P. González, Empowering recommendation technologies through argumentation, in: G. Simari, I. Rahwan (Eds.), Argumentation in Artificial Intelligence, Springer US, Boston, MA, 2009, pp. 403–422.
  * [25] P. Rodríguez, S. Heras, J. Palanca, N. Duque, V. Julián, Argumentation-based hybrid recommender system for recommending learning objects, in: M. Rovatsos, G. Vouros, V. Julian (Eds.), Multi-Agent Systems and Agreement Technologies, Springer International Publishing, Cham, 2016, pp. 234–248.
  * [26] A. J. García, G. R. Simari, Defeasible logic programming: An argumentative approach, Theory and Practice of Logic Programming 4 (2) (2004) 95–138.
  * [27] J. Sabater, C. Sierra, Review on computational trust and reputation models, Artificial Intelligence Review 24 (1) (2005) 33–60.
  * [28] D. Gambetta, Can we trust trust?, in: D. Gambetta (Ed.), Trust: Making and Breaking Cooperative Relations, Oxford: Basil Blackwell, 1988, pp. 213–237.
  * [29] A. Jøsang, R. Ismail, C. Boyd, A survey of trust and reputation systems for online service provision, Decision Support Systems 43 (2007) 618–644.
  * [30] I. Pinyol, J. Sabater-Mir, Computational trust and reputation models for open multi-agent systems: a review, Artificial Intelligence Review 40 (2013) 1–25.
  * [31] Y. Wang, M. P. Singh, Formal trust model for multiagent systems, in: Proceedings of the 20th International Joint Conference on Artificial Intelligence, 2007, pp. 1551–1556.
  * [32] J. Sabater-Mir, C. Sierra, Regret: A reputation model in gregarious societies, in: Proceedings of the 4th Workshop on Deception, Fraud and Trust in Agent Societies, 2001, pp. 61–69.
  * [33] C. Burnett, N. Oren, Position-based trust update in delegation chains, in: Proceedings of the 16th International Workshop on Trust in Agent Societies, 2013\.
  * [34] M. Şensoy, B. Yilmaz, T. J. Norman, STAGE: Stereotypical trust assessment through graph extraction, Computational Intelligence 32 (1) (2016) 72–101.
  * [35] A. Whitby, A. Jøsang, J. Indulska, Filtering out unfair ratings in Bayesian reputation systems, in: Proceedings of the Workshop on Trust in Agent Societies at AAMAS 2004, 2004.
  * [36] A. Jøsang, R. Ismail, The beta reputation system, in: Proceedings of the 15th Bled Electronic Commerce Conference e-Reality: Constructing the e-Economy, 2002, pp. 324–337.
  * [37] R. L. Keeney, H. Raiffa, Decisions with Multiple Objectives: Preferences and Value Tradeoffs, Wiley series in probability and mathematical statistics, John Wiley & Sons, Inc, New York, 1976.