# Evaluating graph-based explanations for AI-based recommender systems

Simon Delarue<EMAIL_ADDRESS>LTCI, Télécom Paris,
Institut Polytechnique de ParisFrance ,  Astrid Bertrand  LTCI, Télécom Paris,
Institut Polytechnique de ParisFrance and  Tiphaine Viard  i3, Télécom Paris,
Institut Polytechnique de ParisFrance

###### Abstract.

Recent years have witnessed a rapid growth of recommender systems, providing
suggestions in numerous applications with potentially high social impact, such
as health or justice. Meanwhile, in Europe, the upcoming AI Act mentions
_transparency_ as a requirement for critical AI systems in order to “mitigate
the risks to fundamental rights”. Post-hoc explanations seamlessly align with
this goal and extensive literature on the subject produced several forms of
such objects, graphs being one of them. Early studies in visualization
demonstrated the graphs’ ability to improve user understanding, positioning
them as potentially ideal explanations. However, it remains unclear how graph-
based explanations compare to other explanation designs. In this work, we aim
to determine the effectiveness of graph-based explanations in improving users’
perception of AI-based recommendations using a mixed-methods approach. We
first conduct a qualitative study to collect users’ requirements for graph
explanations. We then run a larger quantitative study in which we evaluate the
influence of various explanation designs, including enhanced graph-based ones,
on aspects such as understanding, usability and curiosity toward the AI
system. We find that users perceive graph-based explanations as more usable
than designs involving feature importance. However, we also reveal that
textual explanations lead to higher objective understanding than graph-based
designs. Most importantly, we highlight the strong contrast between
participants’ expressed preferences for graph design and their actual ratings
using it, which are lower compared to textual design. These findings imply
that meeting stakeholders’ expressed preferences might not alone guarantee
“good” explanations. Therefore, crafting hybrid designs successfully balancing
social expectations with downstream performance emerges as a significant
challenge.

††copyright: none

##  1\. Introduction

Recommender systems have emerged as fundamental tools for delivering
personalized services to users. These frameworks find application across
diverse domains, ranging from commonplace ones like online library and
e-shopping, to more contentious ones such as finance, law, education or
health. In cases where recommendations carry significant implications for
users, it becomes essential to provide additional explanatory mechanisms. In
Europe, ongoing legal discussions highlight the likelihood of transparency
becoming a requirement for high-risk Artificial Intelligence (AI) systems
(European Commission, 2021). From the academic perspective, several works
underscore a positive correlation between users’ understanding of the model
and their trust in such systems (Sinha and Swearingen, 2002; Tintarev, 2007;
Wilkenfeld, 2014; Zhang and Chen, 2020; Langer et al., 2021).

Explanations aim to address the _“why”_ question in relation to a recommender
system or a specific prediction (Zhang and Chen, 2020). Emerging from two
primary research fields, namely Computer Science (CS) and Human Computer
Interaction (HCI), various forms of explanations, such as text (Wang et al.,
2022), charts (Herlocker et al., 2000; Kouki et al., 2017), matrices (Chen et
al., 2018), hybrid designs (Lundberg and Lee, 2017; Di Giacomo et al., 2021;
Bertrand et al., 2023) or graphs (Wang et al., 2019; Pope et al., 2019; Ying
et al., 2019), have been proposed to enhance user satisfaction toward AI
systems. Nevertheless, determining whether an explanation qualifies as a
“good” one is not straightforward. On the one hand, Miller (Miller, 2019)
argues that “good” explanations should be (i) _contrastive_ , highlighting
contrast with alternatives, (ii) _selected_ , recognizing that people seldom
expect the complete cause of an event and (iii) preferring _causal_ over
probabilistic reasoning. Meanwhile, authors in (Langer et al., 2021) suggest
that it is essential to ensure that explanations align with _stakeholders’
desiderata_ , sometimes referred to as _social expectations_ (Hilton, 1990).

We hypothesize that graphs, as objects that can model selected relational data
and exhibit causality, appears to align intuitively with Miller’s criteria and
may have the ability to fulfill various users’ requirements. More
specifically, the recommendation task can be naturally framed as a _link
prediction_ task in a bipartite graph (see Figure 1) that includes both users
and items. The relevance of graph structures in problem-solving is not novel,
as its roots trace back hundreds of years to Euler’s solution to the
Königsberg bridges problem (Euler, 1741). More recently, within the HCI field,
several works have explored the impact of graph visualization through extended
user studies (Herman et al., 2000; Burch et al., 2020). However, only a few
works (Kouki et al., 2017; Ghazimatin et al., 2020) have explored the
application of such designs in the explanatory context of recommender systems.
In the CS community, when graphs are considered as explanations, they are
often evaluated from an algorithmic perspective, also referred to as
_functionally-grounded evaluation_ (Doshi-Velez and Kim, 2018). This setup
involves the use of machine learning-oriented proxy metrics that operate
without human oversight, contradicting the criteria outlined in (Hilton, 1990;
Langer et al., 2021).

In this work, we seek to bridge the gap between the HCI field, where graph
designs are seldom examined in the context of explainability, and the CS
field, where graphs are ubiquitous in explainable recommender systems – either
as components of models or explanation designs – but are primarily evaluated
from an algorithmic standpoint. To achieve this, we conduct a qualitative user
study to characterize users’ needs in terms of graph-based designs.
Specifically, we gather requirements from users with varying levels of
expertise in AI systems. Leveraging this knowledge, we develop an enhanced
graph-based design and compare it to two commonly used forms of explanations –
textual and SHAP (Lundberg and Lee, 2017)-based explanations – through a
quantitative user study. Through these studies, our goal is to answer the
following research questions:

  * •

RQ1: _What are the different stakeholders’ desiderata regarding graph-based
explanations for recommender systems?_

  * •

RQ2: _How does enhanced graph-based explanation design influence users of AI
recommendations?_

  * •

RQ3: _How does enhanced graph-based explanation design compare to other
explanation designs?_

  * •

RQ4: _How does the user’s expertise level toward AI systems affect their
explanation design preferences?_

Our contributions encompass the following key aspects. Firstly, we investigate
stakeholders’ desiderata for graph-based explanations through a qualitative
user study involving participants with diverse expertise levels, highlighting
the preference for item-based design over user-based design. Secondly, drawing
upon these insights, we build an enhanced graph-based explanation using item-
oriented graph projection. Lastly, we conduct a larger scale quantitative
study to discuss the impact of visual design on understanding, curiosity and
usability. Consequently, we emphasize the contrast between users’ expressed
preference, both qualitatively and quantitatively, for graph-based
explanations and their higher ratings when using dialogic explanations.

##  2\. Related work

###  2.1. Explainable AI for recommender systems

Explainable recommendations offer user personalized item suggestions while
explicitly clarifying _why_ a particular item is being proposed. The design of
explanations for recommendations has been studied for a long time (Herlocker
et al., 2000; Sinha and Swearingen, 2002). Numerous studies emphasized the
correlation between understanding the system and placing trust in it (Sinha
and Swearingen, 2002; Tintarev, 2007; Wilkenfeld, 2014; Zhang and Chen, 2020;
Langer et al., 2021), amplifying the significance of such explanations. While
recommender systems can be crafted to naturally include interpretable elements
(model-intrinsic) (Zhang et al., 2014), our focus is on approaches involving
_post-hoc_ explanations (model-agnostic), also known as post-hoc
interpretability (Lipton, 2018; Miller, 2019).

Designing such explanation is a task situated at the intersection of two
research fields, namely Computer Science (CS) and Human Computer Interaction
(HCI), each with its distinct focus and requirements.

###  2.2. Explainable AI in Computer Science

From the CS perspective, building explainable recommendations involves a
variety of techniques and explanation types. This includes rule mining (Peake
and Wang, 2018), approximation using simple models as with LIME (Ribeiro et
al., 2016), feature importance methods such as SHAP (Lundberg and Lee, 2017),
attention maps (Selvaraju et al., 2017) or graphs (Wang et al., 2019; Pope et
al., 2019; Ying et al., 2019). Concerning graphs, authors often highlight
their natural ability to reveal user-item connectivity (Wang et al., 2019) or
extract small subgraphs containing elements most influential for predictions
(Ying et al., 2019). In this context, justifications for using graphs as
explanations for recommendations seem to align seamlessly with Miller’s
description of good explanations (Miller, 2019).

However, the evaluation of such explanations in the CS field heavily relies on
_functionally-grounded_ approaches (Doshi-Velez and Kim, 2018), _i.e._
algorithmic-oriented techniques that measure proxy metrics such as _fidelity_
or _correctedness_ (Nauta et al., 2023), but lack human control. While such an
evaluation approach offers advantages by saving time and avoiding ethical
concerns potentially associated with human participation, authors in (Miller
et al., 2017; Doshi-Velez and Kim, 2018) emphasize the limitations of these
approaches in terms of “real-world impact” and advocate for their use solely
after conducting user studies. To tackle this issue, recent work integrated
both graph-based explanations and an evaluation framework through a user study
(Zhang et al., 2023). However, the authors restricted their comparison to
graph designs among themselves only, preventing a comprehensive assessment of
the validity of graph explanations against alternative designs.

In this work, we go beyond the conventional algorithmic-oriented evaluation
typically employed in the CS field and introduce both qualitative and
quantitative user studies to evaluate the validity of graph-based explanations
compared to other designs.

###  2.3. Explainable AI in Human Computer Interaction

It is acknowledged that visualization can provide cognitive support through
various mechanisms, _e.g._ helping in pattern discovery, summarizing large
volumes of data, or reducing search time (Tory and Moller, 2004). The HCI
field extensively studies the influence of design choices, with several works
emphasizing their significance in impacting users’ understanding and ability
to contextualize problems. In particular, numerous studies have explored the
impact of graph visualizations and showed the influence of the overall setting
on their performance. For instance, several works demonstrated that node-link
representations were indeed advantageous over matrix representations, when
faced with small graphs and when path-oriented tasks were involved (Ghoniem et
al., 2004, 2005; Okoe and Jianu, 2015; Keller et al., 2006; Okoe et al.,
2018). For other tasks, such as weighted graph comparison (Alper et al., 2013)
or suspicious node detection (McBride and Caldara, 2013), matrix
representations have proven to be a superior choice over node-link diagrams.
More recently, authors in (Di Giacomo et al., 2021) analyzed hybrid
visualizations that combine node-link and matrix representations. Their goal
was to provide a comprehensive tool for analyzing real-world networks that are
globally sparse but locally dense. They showed that in such configurations,
their mixed model overcomes the limitations of using the node-link diagram
alone. While all these approaches systematically involve user study
evaluations, they are not specific to the explanatory context of recommender
systems.

Few works from the HCI field address both graphs and their evaluation as
explanations. In an early study, authors in (O’Donovan et al., 2008) carried
out a user study to assess the impact of various interactive graph-based
representations of a recommender system. They showed that 78% of users _“felt
that the system provided a good explanation of collaborative filtering”_.
However, the graph representation itself was not challenged and the study only
focused on the layout of the system (profile-based or not) and its
interactivity. More recently, authors in (Kouki et al., 2017) involved graphs
to highlight user preferences for item-based explanations. However, their
graphs consist in concentric circle diagrams or pathways between columns,
which we argue do not fully leverage the capabilities of graphs. We draw a
similar conclusion for (Ghazimatin et al., 2020), where authors show
participants’ preference for action-oriented over connection-oriented
explanations, but where graphs are limited to the paths they can exhibit.

In this work, similar to the aforementioned studies, we maintain the
evaluation process that involves user studies to assess design performance.
However, we deviate from them by integrating our analysis directly into the
explanatory context.

##  3\. Study 1: Qualitative exploration of stakeholders’ expectations
regarding graph-based explanations

We seek to answer RQ1 by exploring stakeholders’ expectations regarding graph-
based explanations for AI-based recommender systems. To achieve this, we
conduct a qualitative study involving participants with varying expertise
levels in AI systems, employing a think-aloud case study to collect insights
and remarks.

###  3.1. Study design

We interviewed 12 participants. All participants were volunteers, recruited
through an email campaign within the university with which the authors are
affiliated. We conducted a 30-minute interview with each participant. To
ensure data privacy, participants signed a consent form designed and approved
in collaboration with the Data Protection Officer. Each interview was then
divided into three parts.

In the first part of the session, we assessed the perceived expertise level of
participants regarding AI-based systems in general and recommender systems
specifically, using a preliminary questionnaire. From these answers, we
obtained three distinct groups. The first group consists of _Experts_ (4
participants); people who consider themselves _really familiar_ with AI-based
systems and either have a _precise idea_ of what recommender systems are, or
developed such algorithms. On the other side of the spectrum, another group
includes _non-Experts_ (2 participants); these users consider themselves
_slightly familiar_ with AI-based systems and are only interacting with
recommender systems as users, or have a _general idea_ of how these systems
work. The last group lies in-between these two groups and includes _Insiders_
(6 participants); people who are _familiar_ with AI-based systems and have a
_general idea_ of how recommender systems work.

During the second part of the interview, we evaluated participants’ perceived
understanding of AI-based systems. They answered questions about their
anticipated risks for recommender systems, their comprehension of the
recommendations when they receive some, and their understanding of the
criteria influencing these recommendations.

Lastly, we introduced a task-oriented think-aloud scenario. In this scenario,
we presented participants with an AI-based book recommendation. To increase
participant engagement, we collected their book preferences prior to the
interview and used this information to create a personalized context
incorporated into the AI system, thus making the experiment more realistic.
The system recommendation was presented within a graph-based explanation,
featuring a subset of the participants’ previously read books alongside other
users and their readings. This explanation was displayed as a _bipartite
graph_ , where users were connected to the books they had read, with a
distinct link indicating the system recommendation. Lastly, we provided
information about book preferences using link weighting (see Figure 1 for an
illustration). We did not disclose to the participants the details of the
recommendation algorithm or how we had chosen the other visible users. We
asked the participants to discuss the relevance of the recommendation to them,
their understanding of the elements that led to this recommendation, and what
kind of information was missing to better understand the recommendation.

Figure 1. Bipartite graph-based explanation containing two sets of distinct
nodes. Blue nodes represent users (along with their identifier) and red nodes
represent books (along with their title). A user is linked to a book if the
former has read and rated the latter. Thin links denote low ratings, while
thick links denote high ratings. The blue link corresponds to the system
recommendation.

###  3.2. Results

We extracted key insights from the interviews, focusing on participants’
understanding of the recommendation through the lens of graph-based
explanation. Additionally, we performed an inductive content analysis (Elo and
Kyngäs, 2008) of the notes taken during the interviews, identifying main
themes related to participants’ desiderata toward graph-based explanation.
These results are summarized in Table 1.

####  3.2.1. A high level of perceived understanding, based on three main
criteria

To the question _“To what extent do you understand the suggestions made to you
by AI-based recommendation systems?”_ , the participants, whatever their level
of expertise, said they understood the recommendations in most of the cases.
In the few cases when participants find the recommendations unclear, they
suggest that their profile diverges from a hypothetical _“basic”_ user, or
they propose that the algorithm’s designers intentionally introduced a
mechanism to propose a _“percentage of new things”_ unknown to the users. When
questioned about their understanding of the factors influencing a system to
recommend a specific item, participants identified three main criteria. Both
_Insider_ and _non-Expert_ participants reported that the recommendations are
primarily built based on user characteristics, such as _“the person’s gender,
age”_ or _“the person’s interests”_. Some _Experts_ and a few _Insiders_
believed that the _“similarity”_ between their profile and the profiles of
other users takes precedence in the recommendation algorithm’s decision.
According to this group, the cross-knowledge of a large number of users and
their preferences enables the system to suggest new items. Lastly, some
participants considered that a recommendation is primarily influenced by their
personal history of interactions with the platform. These participants
mentioned factors such as _“clicking on an ad”_ , the _“frequency of viewing”_
, or the _“purchase history”_ as elements at the core of the system’s
suggestions.

####  3.2.2. Strong link between context knowledge and recommendation
understanding

When asked _“Do you think this recommendation is relevant? and why?”_ ,
participants unfamiliar with the item suggested by the system either expressed
a lack of understanding of the recommendation (_“I do not know this book
therefore I don’t know if the recommendation is valuable.”_) or asked for
additional information about the book’s characteristics (_e.g._ author or
literary genre) before answering. Conversely, a participant who is familiar
with the recommendation or, after reviewing its related characteristics
identifies familiar elements (other books by the same author), will
immediately deem the recommendation relevant and give it credibility: _“The
recommendation seems relevant […] because I know the author, and it makes me
want to read the book.”_. In one case, the participant was familiar with the
recommendation, had already read the suggested book, yet found it less
relevant. This judgment was based on the perception that the recommendation
did not align with the literary genre they prefer. In summary, regardless of
their level of expertise, participants tend to draw parallels between the
predicted book and their reading history, considering factors such as literary
genre, period, or author. The analysis of similarities with other users is
only taken into consideration at a later stage and appears to be optional for
the positive or negative judgment of the recommendation. In one instance, the
recommendation aligns with the participant’s literary preferences (known and
appreciated author) and is described as relevant, even if the participant does
not see the connection with the elements provided in the explanation.

####  3.2.3. Graphs as objects modeling both similarities and popularity

Participants’ utilization of the graph-based explanation can be characterized
along two dimensions. Firstly, _Insiders_ and _Experts_ follow the links
between users and books to highlight similarities among users. These
similarities are described through the sharing of literary tastes and
represent the manifestation of their a priori understanding of how a
recommender system operates. In such cases, the weight (thickness) associated
with the links, especially when positive, plays an important role: _“It
visually strikes me a bit”_. Secondly, the graph-based explanation leads to an
understanding of predictions in terms of popularity, _i.e._ participants focus
on the quantity of users who have liked the recommendation. In these cases,
the recommendation is seen as a consequence of the enthusiasm surrounding it,
rather than its relevance to the individual user.

A few remarks about the use of graphs by participants should be noted. The
conclusions drawn regarding user similarities through graph analysis require
time (a few seconds), even for _Experts_ who, at the end of the interview,
considered the graph easy to use. To derive insights about the mechanisms of
the system, _non-Experts_ tend not to analyze the graph through its links and
users, but rather to focus on already-known item characteristics (genre or
author). Finally, none of the participants mention graph-specific notions,
such as _clique_ , _i.e._ fully connected subset elements in a graph or
_density_. Intuitively, such elements could have been used to deeply
understand subgroups of users that led to the recommendation.

####  3.2.4. Graph-based explanations are considered interpretable but should
contain item information

Participants, regardless of their level of expertise, all agree that it would
be useful to have more information about the characteristics of the suggested
item, such as literary genre, period, etc. This would enable the creation of
_“connections between different books”_ and provide a _“general context”_ to
the decision: _“What I miss is the tool’s knowledge about the world.”_ , _“I
lack an idea of which types of books are similar to each other.”_. To address
this, a participant suggests using colors to highlight the proximity between
groups of books. Furthermore, we noticed that the needs for additional item
information align perfectly with the understanding of the recommendation; more
than any other feature, participants require item characteristics that they
can rely on to judge the relevance of a given recommendation.

An _Expert_ and an _Insider_ discussed the value of links representing a weak
attraction to items, mentioning that they helped establish a _“contrast”_ with
other users, thereby enhancing their understanding of _“similarities in their
profile with other users.”_. Some _Insiders_ evoked adding numerical
information on links, rather than playing on thickness. However, the relevance
of information about disliked items, _i.e._ thin links in the graph, is not
shared by all users: _“I don’t really get the thin links in this graph.”_ ,
_“The fact that we have a thick line and a very thin one […], side by side,
confuses me a bit.”_.

_Experts_ find graph explanations _“very easily interpretable”_ or
_“sufficient”_ and do not see the need for them to be _“transformed into
natural language.”_. One of them interprets them as the visual counterpart to
the classic formula found on platforms using recommendation: _“Other users
than you, who liked similar things to you, also liked…”_. Moreover, they _“do
not expect to have an exhaustive representation”_ of the context of the
recommendation. Yet, even _Experts_ can have trouble distinguishing clear
relations with other users; _“I don’t see clearly whether I am close or not to
other users. […] I don’t fully see the connection between me and other
users.”_.

Table 1. Main themes on graph-based explanation design’s desiderata emerging
from interviews.

Explanation |  Experts |  Insiders |  non-Experts
---|---|---|---
Global design choice |  Natural language explanation, no need to transform it into natural language, not structured, contrastive (with link thickness), not exhaustive, [graphs] are really not bad, very easily interpretable, interactions between groups, I don’t fully see the connection between me and other users |  Links between books, themes of books, adding numbered information to links, The fact that we have a thick line and a very thin one […], side by side, confuses me a bit. |  I don’t see what [other users] liked about [an item]
Item-based vs. user-based |  You like this book and there is this other similar book, connections between different books vs. I don’t see if I’m close to other users, I miss visualization of my similarity with other users |  How to find the book characteristics?, I lack an idea of which types of books are similar to each other, thicker links if books are of the same kind, links between books and themes vs. both this user and I did not like this book |  [About books] Do you consider classics? Where do authors come from? Did the books receive prizes?

##  4\. Study 2: Influence of explanation design

To address RQ2, RQ3 and RQ4, we conduct a qualitative user study in which we
investigate the influence of explanation design, among them a graph-based
explanation, on various AI-based recommender system users. An example of our
interface is shown in Figure 5 in the Appendix.

###  4.1. Study design

####  4.1.1. Recommender system

For this purpose, we use the setup introduced in our qualitative experiment
(see Section 3): we build an AI-based book recommender system which goal is to
provide the participant with a book suggestion given pre-selected reading
preferences. We did not share the functioning details of the recommender
system with the participants, but specified that the recommendation they were
given was AI-based.

####  4.1.2. Explanation design

We introduced three different explanation designs, that were detailed to each
participant through a small paragraph, as well as a reading key. We used the
well-known _SHAP_ (Lundberg and Lee, 2017) approach to design a feature
importance oriented explanation design. This choice stems from the popularity
of this method in the Explainable AI (XAI) community as well as for its tight
bounds with the users’ expectations obtained from our first study, _i.e._ book
characteristics are considered important for the quality of the explanation.
We also presented a _Text_ explanation, containing concise item-based
sentences stating _why_ this recommendation was made. This choice was
influenced by recent studies indicating that text explanations, specifically
with item-based wording, were perceived more persuasive than other visual
formats (Kouki et al., 2019) and were effective in providing the user with
personalized feeling, which is correlated with trust in the system (Zhang and
Curley, 2018). Finally, we improved the _Graph_ design introduced in Figure 1
based on participants’ feedback from our qualitative study. Specifically, we
focused on item characteristics by projecting the initial bipartite graph into
an item-oriented one. In this representation, each book is a node, and edges
connect books that share common readers in our database. Additionally, we
incorporated information about book characteristics (literary genre, author,
publication year, etc) through node colors. Consequently, this final graph
representation aligns more closely with users’ expectations. We illustrate
these designs in Figure 2.

(a) Text-based explanation. Item-based wording is chosen accordingly with
results from (Kouki et al., 2019).

(b) Graph-based explanation. Nodes represent the books read by the user,
except for the black-circled node which corresponds to the recommendation.
Node colors denote similarities between book characteristics. A link between
two books exists if they both have been read by some user. Thickness of links
stands for the number of common readers for two books.

(c) SHAP-based explanation (Lundberg and Lee, 2017). Recommendation is
explained through feature importance.

Figure 2. Explanation designs used in the second user study.

####  4.1.3. Experimental conditions

Overall, 66 participants were recruited through two channels: (i) students,
doctoral students and researchers from the author’s university and affiliated
laboratories, and (ii) users of the Reddit forum _“SampleSize”_
111https://www.reddit.com/r/SampleSize/ dedicated to user studies. All
participants were asked to fill out an online form divided into two parts.
First, as in the previous study, participants answered preliminary questions
about their perceived expertise level toward AI-based systems in general and
AI-based recommender systems in particular. We ended up with 11 _non-Experts_
(16.7%), 32 _Insiders_ (48.5%) and 23 _Experts_ (34.8%). We then asked each
participant to choose their favorite book selection from among 5 sets of 10
books and used our AI-based recommender system to suggest a new book. Each
participant was shown all explanation designs for this recommendation (in a
random order), and for each design, they were asked to answer a set of
questions related to our evaluation measures. Finally, we collected
participants’ design preferences by explicitly asking them to rank the three
designs. The whole study lasted around 10 minutes.

In summary, this study is constructed using a Mixed Factorial design,
involving the participants’ _expertise level_ as a between-subject variable
and the _explanation design_ as a within subject variable. Therefore, in the
following analysis, we assess three effects: (i) the influence of the within-
subject variable on the measures, (ii) the influence of the between-subject
variable on the measures, and (iii) the effect of the interaction between the
two factors on the measures.

####  4.1.4. Evaluation constructs

The same set of questions was used for each explanation design. These
questions are built according to previous literature on explanation evaluation
within AI-based frameworks (O’Brien and Cairns, 2015; Hoffman et al., 2018;
Purificato et al., 2021; Bertrand et al., 2023) and are shaped to fit our
specific experimental design. All questions have the same structure; given an
explanation design, we ask the participant _“Please evaluate your level of
agreement with the following sentences.”_. Each participant answers using a
5-point Likert scale ranging from _Strongly disagree_ to _Strongly agree_. We
organized the study around four global constructs (detailed below). We
verified the internal validity of the questionnaire by computing Pearson
correlations between answers within each construct and the total of each
construct (at this stage we discarded results from one question), and its
internal reliability using McDonald’s $\omega$ (McDonald, 2013). Detailed
questions and corresponding categories are provided in Table 2.

Subjective understanding. We measured subjective understanding by asking
participants if they understood the recommendation made by the system or if
they were able to derive insights about its internal mechanics.

Objective understanding. We measured objective understanding of the
recommendations by asking participants about the features used by the system
to provide the recommendation. These questions were specifically designed for
the study but encompass different recommender systems elements that have been
emphasized in our first study as well as in previous studies from the
literature (Kouki et al., 2019; Bertrand et al., 2023), _e.g._ item-based
influence, user-based influence or self-historical information influence.

Usability. Usability was measured by asking participants how difficult the
explanation was to read and understand. We also asked participants about how
easily other people would learn to read this explanation (Alabi et al., 2022).

Curiosity. We measured the curiosity induced by the explanation design using
questions adapted from (O’Brien and Cairns, 2015; Hoffman et al., 2018). We
asked the participants if they were curious to know why the recommender system
did not provide another suggestion, and if the recommendation incited
curiosity.

Table 2. Questions asked to participants for each explanation design, with the
$p$-value corresponding to the validity test (Person’s correlation between
each question and its corresponding global construct) and the McDonald’s
$\omega$ score.

Construct | Reliability $\omega$ | Validity | Question
---|---|---|---
Subjective understanding | 84.4 | $p<.001$ |  I understand why this recommendation was made to me.
$p<.001$ |  I can figure out the internal mechanics of the recommender system.
Objective understanding | 63.5 | $p<.001$ |  I believe this book was recommended to me because it is similar (literary genre, author, etc.) to the books I chose previously.
$p<.001$ |  The fact that I share preferences with other readers only counted a little by the algorithm.
$p<.001$ |  I believe that this book was recommended to me due to its popularity among other readers.
Usability | 48.2 | $p<.001$ |  I would imagine that most people would learn to read this explanation very quickly.
$p<.001$ |  I think the recommender system is interpretable.
_Usability (not used)_ | - | $p>.05$ |  _I found it difficult to read and understand the recommendation context._
Curiosity (0.510) | 60.5 | $p<.001$ |  I am curious about why the recommender system did not make other decisions.
$p<.001$ |  The recommended book incited my curiosity.

###  4.2. Results

We used a repeated-measure analysis of variance (RM-ANOVA) to analyze the
collected measures for each participant. Each measure consisted in the average
of the ratings for the corresponding questions. All measures passed the
sphericity assumption (constant variance across repeated measure), either
using the traditional Mauchly’s test or after applying Greenhouse-Geisser
correction. We confirmed the homogeneity of variance for all levels of the
repeated measures using Levene’s test. When significant effects were observed,
we conducted a post-hoc Bonferroni test for pairwise comparisons. Results are
summarized in Figure 3.

(a) Objective understanding

(b) Subjective understanding

(c) Curiosity

(d) Usability

Figure 3. Results for quantitative study. Vertical bars are confidence
intervals at $95\%$. Significance levels are reported as follows:
$***=p\leq.001,**=p\leq.01,*=p\leq.05$, $\cdot=p\leq.07$ and n.s. non
significant. _Reading key: Objective understanding (Figure 3(a)) was
statistically significantly higher ($p\leq.05$) when participants used textual
explanation rather than graph explanation._

####  4.2.1. Text rather than graph design for higher objective understanding

We found a statistically significant main effect of explanation design on
participants’ objective understanding ($p=.043$). Post-hoc analysis with a
Bonferroni adjustment revealed that text-based design led to significantly
higher objective understanding than graph-based design ($p=.046$) (see Figure
3(a)), but that there was no statistically significant difference for the
levels of objective understanding between graph and SHAP-based designs, nor
between text and SHAP-based designs. The main effect of the expertise level on
objective understanding was not statistically significant ($p=.424$), _i.e._ ,
if we ignore the explanation design being evaluated, _Experts, Insiders_ and
_non-Experts_ gave similar ratings considering the objective understanding
measure. However, within the _Expert_ group, objective understanding was
statistically significantly higher for text-based design than for graph-based
design ($p=.022$).

####  4.2.2. Graph and text designs increase usability.

There was a statistically significant main effect of the explanation design on
usability ($p<.001$), but no statistically significant influence of the level
of expertise on this measure ($p=.398$). Post-hoc tests with a Bonferroni
adjustment revealed that SHAP-based design led to statistically significantly
lower usability compared to graph ($p=.043$) or text-based ($p<.001$) designs
(see Figure 3(d)). However, we did not find any statistically significant
difference between graph and text-based designs ($p=.418$). Within expertise
levels, SHAP-based design led to statistically significantly lower level of
usability than text-based design, for both _Insiders_ ($p=.007$) and _Experts_
($p=.006$). Among _Experts_ , usability was also statistically significantly
higher for text-based design than for graph-based design ($p=.008$).

####  4.2.3. Higher expertise increases curiosity for graph and text designs.

Our analysis determined that there was no statistically significant effect of
explanation design on participants’ curiosity ($p=.514$). However, the
expertise level had a statistically significant influence on curiosity
($p=.016$). Post-hoc tests with a Bonferroni adjustment revealed that being
_Insider_ led to significantly lower levels of curiosity compared to being
_Expert_ ($p=.02$). Specifically, when faced with graph-based designs,
_Experts_ showed statistically significantly higher levels of curiosity than
_Insiders_ ($p=.041$). Similarly, when faced with text-based designs,
_Experts_ were significantly more curious than _Insiders_ ($p=.032$) (see
Figure 3(c)). However, we did not find any statistically significant
difference in curiosity levels between _non-Experts_ and other users.
Furthermore, we did not find any statistically significant difference in
ratings between participants when using SHAP-based design.

####  4.2.4. No effect of explanation design or expertise level on subjective
understanding

We did not observe any statistically significant influence of explanation
design on participants’ subjective understanding ($p=.104$). Similarly, the
expertise level of participants did not have any statistically significant
influence on their subjective understanding ($p=.185$) (see Figure 3(b)).

####  4.2.5. Graph-based design is preferred, regardless of the expertise
level

At the end of the questionnaire, participants were asked to rank explanation
designs according to their preferences. A Chi-Square Goodness of Fit Test was
performed to determine whether the design preferences were equally distributed
across the 6 possible outcomes. Our results revealed that the obtained
proportions significantly differed ($p=.015$). We display in Figure 4 the
number of occurrences of each design at each ranking position. We show that
participants preferred graph-based design more frequently than other designs.
When graph-based design was not ranked in first position, it was ranked in
second position more often than in third position. The second best-ranked
design was text-based explanation. SHAP-based explanation was ranked in last
position more than 35 times. Finally, we explored the relationship between
participants’ expertise level and design preference. We ran a Chi-Square test
and did not find any statistically significant evidence of correlation between
the two variables ($p=.841$).

Figure 4. Number of occurrences of each design at each ranking position.
_Reading key: Graph-based design has been ranked 29 times in #1 position, 24
times in #2 position and 13 times in #3 position._

##  5\. Discussion

###  5.1. Graph vs. textual explanations

According to Miller (Miller, 2019), a “good” explanation should be
_contrastive, selected, dialogic_ and _causal_. We argued that graphs
inherently model causal and contrastive relationships between entities through
their node-link structure. They achieve the _selected_ criterion by
potentially representing a subset of the original data, _e.g._ in the vicinity
of a recommender system’s prediction. Finally, the node or link attributes
they may possess contribute to them being considered dialogic. Consequently,
we aligned with previous studies that acknowledged the strong expressive power
of graphs and the fact that they were especially well-suited for applications
like recommender systems (Afchar et al., 2022).

Initial findings from our qualitative study affirmed these statements and
emphasized the potential effectiveness of graph-based explanations. With the
exception of one _Expert_ who preferred textual explanations, participants
found graph-based design _“very easily interpretable”_ and highlighted the
importance of showcasing _“relations between groups”_. This was further
confirmed quantitatively; when asked for their preferred explanation type, the
majority of participants in our second study favored graph-based explanation
over text or SHAP designs (see Figure 4). Specifically, users expressed a
preference for item-centric graph explanations over user-centric ones,
aligning with results from Kouki _et al._ ’s work (Kouki et al., 2017, 2019),
which compared textual and visual explanations, although not specifically
graphs.

However, despite their expressed preferences for graph-based design, our
second study revealed that participants had significantly higher levels of
objective understanding and usability when using textual designs. Such
contrast between expressed desiderata and measured performance aligns with
previous research (Szymanski et al., 2021; Buçinca et al., 2021) where authors
emphasized users’ preference for visual explanations (though not specifically
graphs) over texts, despite their poorer performance when using the former
kind. A plausible explanation for this tendency may be grounded in the
findings of a recent perception study (Muth et al., 2015) in which authors
showed that interest and attractiveness of a stimuli can be well predicted by
the complexity of this stimuli; in our case, the visual complexity of our
graph explanation, compared to textual explanation could encourage
participants to prefer this design over others. More recent research (Carbon
et al., 2018) explored this direction in the context of graph designs and
further confirmed the close relationship between complex visual structure and
interest.

Overall, our findings suggest that depending solely on stakeholders’ expressed
desiderata for crafting graph-based explanation designs may not be sufficient.
Considering both their preferences and measured performance when using such
explanations could provide a more comprehensive understanding of what
constitutes a good explanation.

###  5.2. Impact of expertise level

Despite its enhanced design tailored to the needs of users with various levels
of expertise, our graph explanation did not lead to significantly higher
ratings on the different measures. Nevertheless, noteworthy insights can be
derived from this visual design. For example, we showed that high expertise
level positively influenced participants’ objective understanding when facing
textual explanation vs. graph explanation, but we did not reveal any
difference in ratings for _non-Experts_. This contrasts with the findings in
(Szymanski et al., 2021) showing how lay users performed better with textual
explanations vs. visual explanations. While authors evoke the plausible
effects of _confirmation bias, i.e._ favoring elements confirming preconceived
ideas, on lay users to explain their results, we did not notice such behavior
in our qualitative study. An interpretation of our results could be that
_Experts_ may be more inclined than _non-Experts_ to have preconceived
notions, or _false narratives_ about the internal functioning of the system, a
behavior that has been particularly observed for experienced users by previous
works (Tversky and Kahneman, 1974; Kaur et al., 2020). These _a priori_ may be
more contradicted by complex designs, such as SHAP or graph, compared to
relatively vague explanations like the textual design we proposed.

We also demonstrated higher curiosity levels for _Experts_ compared to
_Insiders_ , when using graph and text designs, which aligns with previous
research (Niu and Al-Doulat, 2021) stating that curiosity is strongly
associated with intrinsic interest and motivation. We hypothesize that
_Expert_ participants are more engaged in the proposed tasks due to their
interest in the topic. However, this tendency is not observed with SHAP-based
explanation, warranting further analysis for comprehensive understanding.

Our analysis finally revealed that both _Insiders_ and _Experts_ considered
text-based design more usable than SHAP-based design, but only the most
experienced participants found text-based explanations more usable than graph-
based ones. This could be attributed to users with moderate expertise being
more persuadable by graphical design and experiencing some kind of _over-
trust_ (Eiband et al., 2019). Or it may be the result of _Experts_ being more
inclined to over-analyze graph explanations to align with their knowledge of
recommender systems and network layouts. Additionally, we did not find any
statistically significant difference between levels of expertise when
measuring graph usability. This observation aligns with previous research in
the biological field (Singh et al., 2018), showing that novice users could
create and evaluate graph layouts as effectively as domain experts.

In summary, we highlighted how graph-based explanations were subject to
different cognitive biases according to the epxertise level of the end-users.
To address this limit, future work could involve further inclusion of
_cognitive_ factors (Shin and Kim, 2019) in explanations to further enhance
perceived designs.

###  5.3. Limitations

In our quantitative study, we compelled participants to choose from pre-
defined user profiles based on a selection of representative books. This
action further determined the algorithm recommendation and consequently the
content of each explanation. While this approach allows us to use pre-computed
answers, which significantly simplifies the questionnaire procedure, we
acknowledge that it might hinder participants from fully recognizing their
tastes within the recommender system’s choices, potentially influencing their
design preferences. To address this concern, we verified that the user profile
chosen by participants was not correlated with their design preferences. For
this purpose, we ran a Chi-Square test between the two variables and the
results revealed that there is not enough evidence to suggest an association
between the selected user profile and design preferences ($p=.549$).

In this work, we did not impose any time constraint on participants during
their analysis of design, and the only time indication provided was that the
questionnaire should take around 10 minutes to complete. Consequently,
participants had sufficient time to explore and analyze each recommendation,
which may not entirely replicate a real-world scenario where end users require
an explanation to make a decision. Future research aimed at assessing the
generalizability of our findings could involve time-constrained tasks tailored
for specific contexts.

More generally, the results presented in this work are derived from data
related to book recommendations. This simplistic use case facilitated the
building of the study since it mitigated potential ethical concerns. While our
results may have applicability in other domains, additional experiments would
be necessary to confirm this.

##  6\. Conclusion

In this work, we have conducted a qualitative study to understand the needs
for graph-based explanation designs expressed by users with various levels of
expertise. From these results, we built an enhanced graph-based design
emphasizing several key recommendations such as a content-based approach
obtained through bipartite graph projection or the focus on item similarity
rather than user-similarity. We then conducted a quantitative study in which
we investigated the influence of SHAP, textual and graph-based explanation
designs regarding four constructs: objective understanding, subjective
understanding, curiosity and usability. Our results revealed that text-based
explanations significantly improved objective understanding to graph-based
explanation, and that it was specifically marked for users with a higher level
of expertise. On the other hand, we did not find any statistically significant
evidence of influence of explanation design on subjective understanding. We
also showed that both graph and text explanations were considered more usable
than SHAP-based design, and that it was particularly true for users with
middle to high level of expertise. Moreover, we highlighted the influence of
expertise level on curiosity by showing how expert users were more curious
than insiders toward graph and textual explanations. Lastly, we emphasized the
discrepancy between participants’ expressed preferences for graph-based
explanations during qualitative interviews and further confirmed
quantitatively, and their higher ratings regarding understanding or usability
for textual designs. This outcome suggests that solely fulfilling
stakeholders’ desiderata may not suffice to achieve “good” explanations.
Crafting hybrid designs achieving a balance between social expectation and
effective downstream performance emerges as a significant challenge.

##  7\. Ethical concerns

To ensure participants’ privacy, the qualitative study was designed and
approved in collaboration with the Data Protection Officer (DPO) of the
authors’ affiliated school. Anonymized versions of interview notes for data
analysis are securely stored, with access restricted to the authors. We did
not collected any personal information for the quantitative study.

###### Acknowledgements.

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(a) Participants are asked to select a user profile based on a selection of
books.

(b) Participants are reminded of their profile choice and are recommended a
new book.

(c) Example of explanation design description (here graph-based design.)

Figure 5. Example of a participant’s progression through the questionnaire.