# Towards Personalized Answer Generation in E-Commerce via Multi-Perspective
Preference Modeling

Yang Deng<EMAIL_ADDRESS>The Chinese University of Hong KongHong
Kong999077 ,  Yaliang Li<EMAIL_ADDRESS>Alibaba GroupBellevue,
WAUSA ,  Wenxuan Zhang<EMAIL_ADDRESS>The Chinese University of Hong
KongHong Kong ,  Bolin Ding<EMAIL_ADDRESS>Alibaba GroupBellevue,
WAUSA and  Wai Lam<EMAIL_ADDRESS>The Chinese University of Hong
KongHong Kong

###### Abstract.

Recently, Product Question Answering (PQA) on E-Commerce platforms has
attracted increasing attention as it can act as an intelligent online shopping
assistant and improve the customer shopping experience. Its key function,
automatic answer generation for product-related questions, has been studied by
aiming to generate content-preserving while question-related answers. However,
an important characteristic of PQA, i.e., personalization, is neglected by
existing methods. It is insufficient to provide the same “completely
summarized” answer to all customers, since many customers are more willing to
see personalized answers with customized information only for themselves, by
taking into consideration their own preferences towards product aspects or
information needs. To tackle this challenge, we propose a novel Personalized
Answer GEneration method (PAGE) with multi-perspective preference modeling,
which explores historical user-generated contents to model user preference for
generating personalized answers in PQA. Specifically, we first retrieve
question-related user history as external knowledge to model knowledge-level
user preference. Then we leverage Gaussian Softmax distribution model to
capture latent aspect-level user preference. Finally, we develop a persona-
aware pointer network to generate personalized answers in terms of both
content and style by utilizing personal user preference and dynamic user
vocabulary. Experimental results on real-world E-Commerce QA datasets
demonstrate that the proposed method outperforms existing methods by
generating informative and customized answers, and show that answer generation
in E-Commerce can benefit from personalization.

Answer Generation, Product Question Answering, Personalization, E-Commerce

The work described in this article is substantially supported supported by a
grant from the Research Grant Council of the Hong Kong Special Administrative
Region, China (Project Code: 14200719), and Alibaba Group through Alibaba
Research Intern Program. Work done when Yang Deng was an intern at Alibaba.

††copyright: acmcopyright††journal: TOIS††journalyear: 2021††journalvolume:
1††journalnumber: 1††article: 1††publicationmonth: 1††price: 15.00††doi:
10.1145/3507782††journal: TOIS††journalvolume: 40††journalnumber: 2††article:
111††publicationmonth: 4††ccs: Information systems Question answering††ccs:
Information systems Personalization††ccs: Computing methodologies Natural
language generation

##  1\. Introduction

Product Question Answering (PQA) on E-Commerce platforms, such as
Amazon111https://www.amazon.com, eBay222https://www.ebay.com, and
Alibaba333https://www.taobao.com, enables online customers to seek useful
information when making purchase decisions. However, answering product-related
questions relies heavily on the willingness of the customers who have already
bought the same product or the tedious workload of online sales
representatives. In order to address this issue, recent studies (McAuley and
Yang, 2016; Wan and McAuley, 2016) aim at building an automatic PQA system
that takes a product-related question as input, and automatically provides an
answer derived from existing E-Commerce data, such as product reviews,
descriptions, attributes, etc.

Early works in PQA focus on solving the subjective questions (McAuley and
Yang, 2016; Wan and McAuley, 2016; Yu and Lam, 2018), which aim to provide
yes/no answers by identifying customers’ opinions from relevant reviews
towards the given question. Following the general community question answering
(CQA) problem setting, some recent studies (Chen et al., 2019a; Zhao et al.,
2019b) regard relevant reviews as candidate answers and rank a set of review
snippets by measuring the semantic relevance between the question and review
snippets. However, the review snippets selected by these methods fall short of
answering questions effectively, since the user-posted reviews are the general
opinions towards the product but not written specifically for the questions.
Inspired by the recent success on generative open domain question answering
(Nishida et al., 2019; Bi et al., 2019), text generation methods (Chen et al.,
2019b; Gao et al., 2019; Gupta et al., 2019) have been adopted to generate
natural answers to product-related questions. The answers are typically
generated by summarizing the supporting evidence from relevant reviews.

Despite the effectiveness of these methods on automatically providing answers
to product-related questions, they neglect an important characteristic of PQA,
i.e., personalization. Compared with open-domain question answering (Rajpurkar
et al., 2016; Wang et al., 2014), on PQA platforms, there is a large
proportion of subjective questions (McAuley and Yang, 2016) that involve user
preference or require personal information to answer, rather than objective or
factoid questions that look for certain unified answer. Therefore, Carmel et
al. (2018) state that a good PQA system should answer the customer’s questions
with the context of the customer’s encounter history, taking into
consideration his/her preference and interest. Such personalization can make
the answer more helpful for customers and better clarify their concerns about
the product. In order to verify the importance of personalization in PQA, we
conduct a statistical user study by analyzing the Amazon QA datasets (Wan and
McAuley, 2016), collected from the real-world PQA platform. The user study is
conducted as follows:

  * (1)

We randomly sample 100 helpful QA pairs from each of three different product
categories, including Electronics, Home&Kitchen, Sports&Outdoors, i.e., 300
questions in total. Note that a helpful QA pair means that the user-provided
answer receives more than two votes as well as more upvotes than downvotes.

  * (2)

We manually label the QA pairs by two criteria: (i) Is the question subjective
or objective; (ii) Whether the answer reflects any specific user preference?
If yes, does the answer reflects the personal preference of user experience or
product aspects?

  * (3)

Each QA pair is labeled by two annotators and the disagreements (about 4%) are
settled by discussions.

The statistics of the user study are presented in Figure 1. There are several
notable observations:

  * (1)

There are a larger proportion of subjective questions (56.8%) in PQA than
objective questions (43.2%), which is similar to the findings in previous
studies (McAuley and Yang, 2016; Wan and McAuley, 2016).

  * (2)

Among these helpful QA pairs, we observe that 80.9% of answers can somewhat
reflect specific personalized information, including personal experience or
preferred product aspects. Especially for those subjective questions, almost
all the helpful answers involve such personalized information. Moreover, for
those objective questions, there are still half of the helpful answers that
contain personalized information.

  * (3)

Among those answers that reflect user preferences, there are two different
perspectives of user preferences: one is concerned with personal experience
and the other one is concerned with preferred product aspects. Besides, it can
be observed that there are also a large number of answers that contain both of
them.

To this end, we study personalized answer generation for PQA, which can not
only generate natural language answers based on relevant reviews, but also
provide customized answers to different customers by exploring and modeling
user preference. To the best of our knowledge, this work is the first attempt
to marry product question answering with personalization.

Figure 1. The Statistics of User-generated Questions and Answers on Amazon QA
(best viewed in color). Subj. and Obj. denote subjective and objective
questions respectively. Exp. and Asp. denote the answers with personal
experience or preferred product aspects, respectively, while None denotes the
answers that cannot reflect any user preference.

Meanwhile, different customers typically have certain preferences over product
aspects or information needs (Chen et al., 2019c; Li et al., 2019b), leading
to various expectations for the provided answers. As shown in a real case
depicted in Figure 2, for the same question about the product Tokina Lens,
different customers may find different answers helpful to themselves. For
example, User1 may focus more on the price of the product, while User3 cares
more about the weight. Besides, User2 seeks information about a specific
camera model. This example shows that customers have various preferences and
information needs, and the generated answers should take into consideration
such diversity. Therefore, it is necessary to obtain various information that
different customers are interested in from massive product-related data for
enriching the provided answers in PQA. This requires the ability of
comprehensive user preference modeling, rather than targeting at a single
perspective of personalization. Due to the nature of E-Commerce platforms,
there are a considerable amount of valuable user-profiling data for user
modeling, including user-generated content (UGC), user-item interaction,
browsing and clicking history. This leads to extensive studies on
personalization in E-Commerce scenario, such as personalized recommendation
(Gu et al., 2020), review generation and summarization (Ni and McAuley, 2018;
Li and Tuzhilin, 2019; Li et al., 2019a), product description generation (Chen
et al., 2019c; Elad et al., 2019), etc. To achieve comprehensive user
modeling, we leverage historical UGC (including user-posted reviews, answers,
and questions) to model multi-perspective user preferences for personalizing
the generated answer in PQA.

Figure 2. A real case from Amazon QA and Review datasets (Wan and McAuley,
2016; Ni et al., 2019). Personalized answer generation is motivated by that
different customers may find different answers more helpful, in terms of their
own preferences towards product aspects or information needs.

There are two main challenges that need to be tackled for modeling multi-
perspective user preference for PQA: (i) How to obtain user preferences from
historical UGC in terms of different perspectives? Motivated by the user study
in Figure 1, we investigate approaches to capture users’ experience knowledge
and preferred product aspects from their historical UGC. Historical UGC can
not only be regarded as personal knowledge bases that contain users’
experience knowledge, but also serve as the corpus for topic modeling of
different product aspects. (ii) How to aggregate the multi-perspective user
preference information for answer generation in PQA? Existing works on
personalized text generation typically focus on modeling a specific
perspective of user preference, such as learning user features (Chen et al.,
2019c; Zeng et al., 2019; Elad et al., 2019) or imitating user writing styles
(Li et al., 2019a; Li et al., 2019b; Li and Tuzhilin, 2019), by adopting pre-
defined user categories or relying on fixed frequency-based user vocabulary
for user preference modeling. As for personalized answer generation in PQA,
these approaches may fall short of revealing diverse and dynamic preferences
from person to person. And it is likely to produce stereotype preference, such
as dividing customers into different groups or based on gender or age.
Instead, it is required the capability of selectively summarizing the most
important persona information when modeling multi-perspective user preference.

In this work, we propose a novel Personalized Answer GEneration method (PAGE)
that explores historical user-generated content to comprehensively model
multi-perspective user preference for PQA. We first retrieve question-related
user history based on the relevancy to the given question, and leverage such
user history content to incorporate valuable personal historical information
as external knowledge for knowledge-level preference modeling. Then we employ
Gaussian Softmax distribution Model (GSM) based latent user preference
modeling to capture aspect-level user preference. This enables us to learn
individual preference information as well as provide explicit user-preferred
product aspects. Meanwhile, a preference-based user vocabulary is dynamically
built for providing answers with specific expressions that reflect user
preference in vocabulary-level. Finally, we develop a persona-aware pointer
network to summarize multi-perspective personal information for generating
personalized answers, including knowledge-level, aspect-level, and vocabulary-
level user preferences. Experimental results on real-world E-Commerce QA
datasets show that the proposed method outperforms existing methods on both
answer generation and personalization in PQA, by comprehensively taking into
account multi-perspective user preference.

In summary, our main contributions are as follows:

  * •

We investigate a new problem, the personalization of answer generation in
E-Commerce, which aims at exploring personal preference from user-generated
data to generate personalized answers for different customers.

  * •

We propose a novel Personalized Answer GEneration (PAGE) method with multi-
perspective preference modeling to comprehensively model user preference at
knowledge-, aspect-, and vocabulary-level from textual personal data.

  * •

We propose a persona-aware pointer-generator network that bridges neural topic
modeling and pointer network to dynamically capture user preference and learn
a preference-based user vocabulary for personalized text generation.

  * •

A real-world E-Commerce QA dataset with historical UGC is constructed for the
personalized answer generation studies in PQA444Available at
https://github.com/dengyang17/TOIS-PAGE. The proposed method outperforms
existing methods on real-world E-Commerce QA datasets and effectively
generates informative, diverse, and persona-aware answers.

The rest of the article is organized as follows. Section 2 reviews related
works. Section 3 introduces the problem definition of the personalized answer
generation task in E-Commerce scenario. The proposed method for personalized
answer generation is described in Section 4. Experimental details and results
are discussed in Sections 5 and 6, respectively. Finally, we draw a conclusion
about the work and provide some possible directions for future works in
Section 7.

##  2\. Related Works

This work focuses on a new problem, namely the personalization of answer
generation for product-related question answering, which is closely related to
the studies of Product Question Answering, Open-domain Generative QA,
Personalized Text Generation, and Personalization with UGC in E-Commerce.

###  2.1. Product Question Answering

Existing PQA studies typically fall into two problem settings, which are based
on the assumption that candidate user-posted answers exist or not. Those PQA
studies assuming the existence of candidate answers basically follow the
traditional CQA problem setting (Shen et al., 2018; Deng et al., 2022) to rank
a list of user-posted answers (Yu et al., 2018; Kulkarni et al., 2019; Zhang
et al., 2020a). Yu et al. (2018) study transfer learning techniques to
leverage cross-domain knowledge for E-Commerce answer ranking. Zhao et al.
(2019b) and Zhang et al. (2020a) exploit product reviews as external knowledge
to enhance the answer ranking in PQA. Besides, Zhang et al. (2020c) follow
previous review helpfulness prediction studies (Fan et al., 2019a; Chen et
al., 2019d) and formulate the answer helpfulness prediction task for finding
helpful answers from a set of candidate answers. Zhang et al. (2020b) focus on
the factoid questions in PQA and study as a fact checking problem. The other
group of PQA studies exploits product reviews as the supporting facts to
automatically produce answers for those unanswered questions (McAuley and
Yang, 2016; Wan and McAuley, 2016). These works can be further categorized
into retrieval-based methods and generation-based methods. Retrieval-based
methods (Yu et al., 2012; Zhao et al., 2019b; Chen et al., 2019a) aim at
selecting review sentences as the answer by measuring the semantic relevance
between the question and reviews. Text generation methods (Chen et al., 2019b;
Gao et al., 2019, 2021; Deng et al., 2020c) employ text generation techniques
to generate natural sentences from the relevant product reviews for answering
the given question. Chen et al. (2019b) propose a gated attention mechanism to
capture the question-related information from the reviews for denoising in
answer generation. Gao et al. (2019) propose an adversarial learning framework
to identify whether the generated answer matches the facts, which is further
enhanced by a review-reasoning module and prototype-editing (Gao et al.,
2021). Feng et al. (2021) propose a review-attribute heterogeneous graph
neural network to model the logical relation of multi-type text in PQA.
However, existing studies neglect an important characteristic of PQA, i.e.,
personalization.

###  2.2. Open-domain Generative QA

Different from extractive QA (Rajpurkar et al., 2016; Deng et al., 2022,
2020d), which aims to select sentences or extract certain text spans from the
source document to answer the given question, generative QA (Nguyen et al.,
2016; Fan et al., 2019b) generates natural sentences as the answer. In factoid
QA, some latest works (Bauer et al., 2018; Indurthi et al., 2018) adopt
abstractive summarization methods, based on pointer-generator framework (See
et al., 2017), to generate an abstractive answer. Nishida et al. (2019)
propose a multi-style abstractive summarization model for generative QA with
multi-task learning. Bi et al. (2019) incorporate external knowledge to
generate a natural answer. Recently, pre-trained language models (Lewis et
al., 2020; Izacard and Grave, 2020) are leveraged to tackle the open-domain
generative factoid QA with open-retrieval from large-scale knowledge bases,
e.g., Wikipedia. On the other hand, several efforts have been made on tackling
non-factoid generative QA over supporting documents, which mainly focus on
“how” or “why” type questions (Deng et al., 2020a; Ishida et al., 2018; Deng
et al., 2020b) or aim at generating a conclusion or explanation for the
concerned question (Fan et al., 2019b; Nakatsuji and Okui, 2020). In
E-Commerce scenario, the answer to the given question is often not unique,
since it is expected to meet the diverse personal interests and information
needs of different users.

###  2.3. Personalized Text Generation

In recent years, personalized textual content generation has attracted
research interest from various domains. Personalized dialogue generation
(Zhang et al., 2018) is one of the most active research topics in personalized
text generation, which aims at generating persona-aware responses in multi-
turn conversation. They mainly focus on exploiting the prerequisite personal
textual information to control the persona-consistency (Song et al., 2020; Xu
et al., 2020) and enhance the diversity in response generation (Song et al.,
2019; Wu et al., 2020). Another widely-studied area is personalized review
generation (Ni and McAuley, 2018; Sun et al., 2020; Li et al., 2020) and
summarization (Li et al., 2019a; Li et al., 2019b). Ni and McAuley (2018) and
Li et al. (2019a) leverage specific user embedding approaches to incorporate
personal information into review generation and summarization, respectively.
Li and Tuzhilin (2019) and Li et al. (2019b) combine autoencoder and
discriminator and employ user vocabulary to imitate user writing styles when
generating or summarizing product reviews, respectively. Besides, there are
some new applications on personalized text generation, including personalized
product description generation (Chen et al., 2019c; Elad et al., 2019),
recommendation explanation generation (Musto et al., 2019; Ni et al., 2019;
Zhao et al., 2019a), personalized recipe generation (Majumder et al., 2019),
etc. The personalization becomes more appealing in PQA, since there are
various perspectives of user preferences that need to be involved in the
provided answers.

###  2.4. Personalization with UGC in E-Commerce

As a user-centric application, E-Commerce platforms preserve a considerable
amount of valuable user-profiling and user history data for modeling diverse
user preferences or behaviors. Such user data includes user-generated content
(UGC), user-item interaction, browsing and clicking history, etc. This leads
to extensive studies on different tasks with personalization in E-Commerce
scenario, including the personalized text generation studies that we have
mentioned in Section 2.3, like review generation and summarization (Ni and
McAuley, 2018; Li and Tuzhilin, 2019; Li et al., 2019a), product description
generation (Chen et al., 2019c; Elad et al., 2019). One of the most
extensively studied research topics involving personalization in E-Commerce is
the personalized recommendation (Zhang et al., 2019; Gu et al., 2020), which
typically explores the utilities of user-item interaction data to model the
user preferences towards different items. Recently, several attempts (Sachdeva
and McAuley, 2020; Gao et al., 2020; Guan et al., 2019) have been made on
leveraging UGC data, such as reviews, for personalized recommendation. The UGC
data can not only be served as a measurement of semantic relevance for user
preference modeling (Zeng et al., 2021), but also be adopted as the explicit
justifications for explainable recommendation (Cheng et al., 2019). Another
trending research area in E-Commerce is the personalized product search (Ai et
al., 2017, 2019; Guo et al., 2019), where the UGC data is also beneficial to
the semantic product matching (Bi et al., 2020). In this paper, we investigate
the utilities of UGC data for the personalization in PQA.

Table 1. Notations Notation |  Definition
---|---
$\bm{X_{q}}$ |  the embedding matrix of the question
$\bm{X_{y}}$ |  the masked embedding matrix of the answer
$\bm{X_{f_{k}}}$ |  the embedding matrix of the $k$-th fact snippet
$\bm{X_{h_{k}}}$ |  the embedding matrix of the $k$-th history snippet
$\bm{d}$ |  the bag-of-words (BoW) representation of the $\mathcal{H}^{*}$
$\bm{T}$ |  the global preference embeddings
$\bm{V}$ |  the global vocabulary embeddings
$\bm{M_{q}}$ |  the question memory vector
$\bm{M_{f}}$ |  the fact memory vector
$\bm{M_{h}}$ |  the persona history memory vector
$\bm{M_{t}}$ |  the persona preference memory vector
$\bm{M_{p}}$ |  the persona vocabulary vector
$\bm{s_{t}}$ |  the decoder output at the $t$-th step

##  3\. Problem Definition

We present the problem of personalized answer generation for product-related
questions. The data consists of users, items, questions, answers, supporting
facts (including review snippets, product descriptions, and attributes), and
historical user-generated content (including reviews, answers, questions). The
whole training corpus is denoted by
$\mathcal{D}=\\{\mathcal{U},\mathcal{I},\mathcal{Q},\mathcal{A},\mathcal{F},\mathcal{H}\\}$,
where $\mathcal{U}$ and $\mathcal{I}$ are the sets of users and items
respectively, $\mathcal{Q}$ and $\mathcal{A}$ are the sets of questions and
corresponding user-written answers respectively, $\mathcal{F}_{i}$ is the set
of supporting facts for the item $i$ in $\mathcal{I}$, and $\mathcal{H}_{j}$
is the set of historical UGC from the user $j$ in $\mathcal{U}$. Due to the
large number of reviews, we extract top-$k$ snippets $\mathcal{F}^{*}_{i}$ and
$\mathcal{H}^{*}_{j}$ from $\mathcal{F}_{i}$ and $\mathcal{H}_{j}$, which are
most relevant to the given question based on information retrieval (IR)
techniques.

With the above definitions, our problem can be formally defined as: Given a
product-related question $q\in\mathcal{Q}$ concerning the item $i$, we aim to
automatically generate a personalized and informative answer $y$ for the
specific user $j$, based on the relevant supporting facts
$\mathcal{F}^{*}_{i}=\left\\{f_{1},f_{2},...,f_{k}\right\\}_{i}$ and
historical UGC
$\mathcal{H}^{*}_{j}=\left\\{h_{1},h_{2},...,h_{k}\right\\}_{j}$. For
simplicity, we omit $i$ and $j$ in the following notations. Apart from the
above notations, the notations used in the methodology are listed in Table 1.

##  4\. Personalized Answer Generation

As shown in Figure 3, the proposed method, PAGE, consists of four components:

  * •

(1) Basic Encoder-decoder Architecture is the base model for general answer
generation without personalization, which takes the question $q$ and the
retrieved supporting facts $\mathcal{F}^{*}$ as input and outputs the decoded
vocabulary probability distribution $P_{v}(a)$;

  * •

(2) Persona History Incorporation module incorporates historical UGC as
external knowledge for knowledge-level user preference modeling, which takes
the retrieved historical UGC $\mathcal{H}^{*}$ as input and outputs the
persona history memory vector $M_{h}$;

  * •

(3) Persona Preference Modeling module employs neural topic model to capture
latent aspect-level user preference as well as dynamically provide preference-
based user vocabulary, which takes the BoW representation $d$ of the
$\mathcal{H}^{*}$ as input, and outputs the persona preference memory vector
$M_{t}$ and the persona vocabulary vector $M_{p}$;

  * •

(4) Persona Information Summarizer module summarizes the multi-perspective
user preference information for generating personalized answers, which
aggregate the multi-perspective information to calculate the final vocabulary
probability distribution $P(y_{t})$.

Figure 3. Overview of the proposed method PAGE, including four components: (1)
Basic Encoder-decoder Architecture, (2) Persona History Incorporation, (3)
Persona Preference Modeling, and (4) Persona Information Summarizer.

###  4.1. Basic Encoder-decoder Architecture

We first describe the base model that only performs general answer generation
without the consideration of personal information. We combine the Transformer
(Vaswani et al., 2017) and the BiDAF (Bidirectional Attention Flow) (Seo et
al., 2017) as the basic encoder-decoder architecture.

####  4.1.1. Self-Attentive Encoder Layer

For the encoder, we use a shared self-attentive encoder to compute the
representations of the question, supporting facts and history content
separately. Each self-attentive encoder layer consists of three components:
(i) The layer normalization, which is defined as LayerNorm$(\cdot)$. (ii) The
multi-head attention, which is defined as MultiHead$(\bm{Q},\bm{K},\bm{V})$,
where $\bm{Q},\bm{K},\bm{V}$ are query, key, and value, respectively. (iii)
The feed-forward network with ReLU activation, which is defined as
FFN$(\cdot)$. And the encoder is composed of a stack of $N_{e}$ identical
layers. Take the first encoder layer for question encoding for example:

(1)
$\displaystyle\bm{C_{*}}=\text{MultiHead}(\bm{X_{*}},\bm{X_{*}},\bm{X_{*}}),$
(2)
$\displaystyle\bm{O_{*}}=\text{LayerNorm}(\text{FFN}(\bm{C_{*}})+\bm{X_{*}}),$

where $*\in\\{q,f_{1},f_{2},...,f_{k}\\}$ represents the word sequence of the
question and each supporting fact snippets, $\bm{X_{*}}$ denotes their
embeddings, and $\bm{O_{*}}\in\mathbb{R}^{L_{*}\times d_{h}}$ is the output of
the encoder layer. $L_{*}$ and $d_{h}$ denote the length of the corresponding
sequence and the dimension of the hidden layer, respectively.

####  4.1.2. Bi-Attention Layer

After obtaining the encoded representation for all the input sequences, we
apply a bidirectional attention layer (BiAttention) (Seo et al., 2017) to
refine the question-related information in the supporting facts. For each
supporting fact snippet $f_{k}$, we derive a similarity matrix
$\bm{U_{f_{k}}}\in\mathbb{R}^{L_{q}\times L_{f}}$ between the question and
snippet:

(3)
$\bm{U_{f_{k}}^{(ij)}}=\bm{\omega_{U}}^{\intercal}[\bm{O_{q}^{(i)}};\bm{O_{f_{k}}^{(j)}};\bm{O_{q}^{(i)}}\circ\bm{O_{f_{k}}^{(j)}}],$

where $\bm{\omega_{U}}\in\mathbb{R}^{3d_{h}\times 1}$ is a trainable weight
vector, $\circ$ denotes element-wise multiplication, and [;] denotes the
concatenate operation. Then, we can obtain the row and column normalized
similarity vectors as the question-to-fact and fact-to-question attention
weights, $\bm{A_{f_{k}}}$ and $\bm{B_{f_{k}}}$, respectively:

(4) $\displaystyle\bm{A_{f_{k}}}=\text{softmax}(\bm{U_{f_{k}}}),$ (5)
$\displaystyle\bm{B_{f_{k}}}=\text{softmax}(\bm{U_{f_{k}}}^{\intercal}).$

After being weighted by the attentions, the attended question and fact
representations will be:

(6)
$\displaystyle\widetilde{\bm{O_{q}}}=\bm{A_{f_{k}}}^{\intercal}\bm{O_{q}},$
(7)
$\displaystyle\widetilde{\bm{O_{f_{k}}}}=\bm{B_{f_{k}}}^{\intercal}\bm{O_{f_{k}}}.$

And we use a simple concatenation to get the final fact memory representation
$\bm{M_{f_{k}}}\in\mathbb{R}^{L_{f}\times 4d_{h}}$:

(8)
$\begin{split}\bm{M_{f_{k}}}=&~{}\text{BiAttention}(\bm{O_{f_{k}}},\bm{O_{q}})\\\
=&~{}[\bm{O_{f_{k}}};\widetilde{\bm{O_{q}}};\bm{O_{f_{k}}}\circ\widetilde{\bm{O_{q}}};\bm{O_{f_{k}}}\circ\widetilde{\bm{O_{f_{k}}}}].\end{split}$

####  4.1.3. Decoder Layer

Similar to the encoder, we adopt a stack of $N_{d}$ Transformer decoder layers
that contain multi-head attention and feed-forward network as the decoder. The
difference is that the decoder employs not only the source text information,
i.e., the question, but also the information from supporting facts to generate
the answer. For each decoder layer:

(9)
$\displaystyle\bm{H_{y}}=\text{MultiHead}(\bm{X_{y}},\bm{X_{y}},\bm{X_{y}}),$
(10)
$\displaystyle\bm{H_{q}}=\text{MultiHead}(\bm{H_{y}},\bm{O_{q}},\bm{O_{q}}),$
(11)
$\displaystyle\bm{H_{f}}=\text{MultiHead}(\bm{H_{q}},\bm{M_{f}},\bm{M_{f}}),$
(12) $\displaystyle\bm{O_{dec}}=\text{FFN}(\bm{H_{f}}),$

where $\bm{X_{y}}$ is the masked answer embedding, $\bm{M_{f}}$ is the
concatenated outputs of all the supporting fact snippets
$\bm{M_{f}}=[\bm{M_{f_{1}}},\bm{M_{f_{2}}},...,\bm{M_{f_{k}}}]\in\mathbb{R}^{kL_{f}\times
d_{h}}$, and $\bm{O_{dec}}$ is the decoder output. For simplicity, we omit the
layer normalization and residual add operation (Vaswani et al., 2017) in the
equation.

Finally, the decoder output $\bm{O_{dec}}$ is projected into the vocabulary
with a softmax function to obtain the vocabulary distribution:

(13) $P_{v}(a)=\text{softmax}(\bm{W_{v}}\bm{O_{dec}}+\bm{b_{v}}),$

where $\bm{W_{v}}\in\mathbb{R}^{d_{h}\times|V|}$ and
$\bm{b_{v}}\in\mathbb{R}^{|V|}$ are the projection parameters to be learned,
and $|V|$ is the vocabulary size.

###  4.2. Persona History Incorporation

Figure 4. Basic Encoder-decoder Architecture with Persona History
Incorporation (PHI). An additional Multi-Head Attention layer is added into
the decoder for incorporating the persona history information.

In order to generate a personalized and informative answer for a specific
user, it is insufficient to only exploit the general supporting facts. In
real-world applications, customers usually expect to receive some practical
suggestions or seek certain information needs according to their own
experience. For example, users would like to know about the specific answer
corresponding to certain camera model as the example in Figure 2, or under
particular situations. And this prior knowledge can be discovered from user
history.

To this end, we leverage question-related historical user-posted content
$\mathcal{H}^{*}$ as external knowledge for incorporating their personal
history. We employ the same shared self-attentive encoder and bi-attention
mechanism to encode the retrieved historical content snippets:

(14)
$\displaystyle\bm{C_{h_{k}}}=\text{MultiHead}(\bm{X_{h_{k}}},\bm{X_{h_{k}}},\bm{X_{h_{k}}}),$
(15)
$\displaystyle\bm{O_{h_{k}}}=\text{LayerNorm}(\text{FFN}(\bm{C_{h_{k}}})+\bm{X_{h_{k}}}),$
(16)
$\displaystyle\bm{M_{h_{k}}}=\text{BiAttention}(\bm{O_{h_{k}}},\bm{O_{q}}),$

where $\bm{M_{h_{k}}}$ is the encoded persona history memory vector. Then, we
can incorporate the user history information into the decoding phase for
knowledge-level user preference modeling. The basic encoder-decoder
architecture with Persona History Incorporation (PHI) module is depicted in
Figure 4.

###  4.3. Persona Preference Modeling

As discussed in Section 1, only modeling knowledge-level user preference is
insufficient for personalized answer generation. Different users also may have
their own latent aspect-level preference or explicit vocabulary-level
preference towards various answers. Instead of using pre-defined user
categories or fixed frequency-based user vocabulary, persona preference model
(PPM) module employs neural topic model to capture latent aspect-level user
preference as well as dynamically provide preference-based user vocabulary.
The basic encoder-decoder architecture with Persona Preference Modeling (PPM)
module is depicted in Figure 5.

To be specific, Gaussian Softmax distribution model (GSM) (Miao et al., 2017)
is adopted for unsupervised topic modeling, which is based on variational
autoencoder (VAE) (Kingma and Welling, 2014). Formally, similar to persona
history incorporation module, PPM also adopts a set of question-related
historical UGC $\mathcal{H}^{*}$ as the input. The difference is that PPM
processes the collection of each user’s UGC into bag-of-words (BoW)
representation $\bm{d}$, instead of using the original natural sentences. The
purpose is to enable the interpretation of latent aspect-level preference
modeling as well as provide the foundation of vocabulary-level preference
modeling. In the neural variational inference process, the input $\bm{d}$ is
first encoded into continuous Gaussian variables $\bm{\mu}$ and $\bm{\sigma}$
via a multi-layer perceptron:

(17) $\displaystyle\mu=\text{FFN}_{\bm{\mu}}(\text{FFN}_{d}(\bm{d})),$ (18)
$\displaystyle\text{log}(\bm{\sigma}^{2})=\text{FFN}_{\sigma}(\text{FFN}_{d}(\bm{d})).$

Figure 5. Basic Encoder-decoder Architecture with Persona Preference Modeling
(PPM). An additional Multi-Head Attention layer is added into the decoder for
incorporating the persona preference information.

The prior $p(\bm{x})=\mathcal{N}(\bm{\mu},{\bm{\sigma}}^{2})$ is a diagonal
Gaussian distribution with mean $\bm{\mu}$ and $\bm{\sigma}^{2}$ as the
diagonal of the covariance matrix. By using the Gaussian prior distribution,
we can build an unbiased gradient estimator for the variational distribution
with the re-parameterization trick (Kingma and Welling, 2014). Then, we use
latent variable $\bm{\theta}\in\mathbb{R}^{K}$, which is derived by the
Gaussian Softmax operation over the prior distribution $\bm{x}$, to denote the
probability distribution over the aspect-level user preference cluster:

(19) $\displaystyle\bm{x}\sim\mathcal{N}(\bm{\mu},\bm{{\sigma}}^{2}),$ (20)
$\displaystyle\mathbf{\bm{\theta}}=\text{softmax}(\bm{W_{x}}\bm{x}+\bm{b_{x}}),$

where $K$ is the predefined number of aspect clusters, $\bm{W_{x}}$ and
$\bm{b_{x}}$ are the weights and bias of linear transformation to be learned.
In practise, we reparameterise $\bm{x}=\bm{\mu}+\epsilon\cdot\bm{\sigma}$ with
the sample $\epsilon\in\mathcal{N}(0,I)$. We use $q(\bm{\theta}|\bm{d})$ to
denote the variational inference process approximating the true posterior
$p(\bm{\theta}|\bm{d})$.

We denote the aspect assignment as $z_{n}$ for the observed word $w_{n}$. A
neural network is used to parameterise the multinomial aspect distribution,
and the generative process for $\bm{d}$ is:

(21) $\displaystyle
z_{n}\sim\text{Multi}(\bm{\theta}),~{}\text{for}~{}n\in[1,|V_{bow}|],$ (22)
$\displaystyle
w_{n}\sim\text{Multi}(\bm{\beta_{z_{n}}}),~{}\text{for}~{}n\in[1,|V_{bow}|],$

where $\bm{\beta_{z_{n}}}$ denotes the aspect distribution over words given
aspect assignment $z_{n}$, and $V_{bow}$ is the vocabulary for BoW model.
Then, the marginal likelihood is computed by:

(23)
$p(\bm{d}|\bm{\mu},\bm{\sigma},\bm{\beta})=\int_{\bm{\theta}}p(\bm{\theta}|\bm{\mu},\bm{\sigma})\prod_{n}\sum_{z_{n}}p(w_{n}|\bm{\beta_{z_{n}}})p(z_{n}|\bm{\theta})d\bm{\theta}.$

In order to explicitly model the user preference, the aspect distribution over
words $\bm{\beta}$ is constructed by the product of the global preference
embeddings $\bm{T}\in\mathbb{R}^{K\times d_{h}}$ and the global vocabulary
embeddings $\bm{V}\in\mathbb{R}^{|V_{bow}|\times d_{h}}$, instead of being
regarded as a single parameter matrix:

(24) $\bm{\beta}=\text{softmax}(\bm{T}\bm{V}^{\intercal}),$

where $\bm{T}$ and $\bm{V}$ are randomly initialized embeddings to be learned.
Therefore, $\bm{\beta}\in\mathbb{R}^{K\times|V_{bow}|}$ can be regarded as the
semantic similarity matrix between aspects and words. Then we can decouple the
aspect-level and vocabulary-level user preference easily. We reconstruct a new
vector $\bm{d^{\prime}}$ by

(25) $\bm{d^{\prime}}=\bm{\theta}\cdot\bm{\beta},$

which is served as the user vocabulary distribution based on the persona
preference modeling.

The aspect distribution $\bm{\theta}$ and the vocabulary distribution
$\bm{d^{\prime}}$ are combined with the global preference embeddings $\bm{T}$
and the global vocabulary embeddings $\bm{V}$ to build the persona preference
memory vectors $\bm{M_{t}}$ and persona vocabulary vectors $\bm{M_{p}}$ for
each user:

(26) $\displaystyle\bm{M_{t}}=\bm{\theta}\cdot\bm{T},$ (27)
$\displaystyle\bm{M_{p}}=\bm{d^{\prime}}\cdot\bm{V}.$

The persona preference memory vectors $\bm{M_{t}}$ are further integrated into
the answer decoding phase for integrating aspect-level user preference, while
the persona vocabulary vectors $\bm{M_{p}}$ are used to enhance the generated
answers with user-preferred language styles.

For PPM, the objective function is defined based on the negative variational
lower bound (Miao et al., 2017) according to Equation (23):

(28)
$\begin{split}\mathcal{L}_{PPM}=&-\mathbb{E}_{q(\bm{\theta}|\bm{d})}\left[\sum_{n=1}^{|V_{bow}|}\text{log}\sum_{z_{n}}\left[p(w_{n}|\bm{\beta_{z_{n}}})p(z_{n}|\bm{\theta})\right]\right]\\\
&+\mathcal{D}_{KL}[q(\bm{\theta}|\bm{d})||p(\bm{\theta}|\bm{\mu},\bm{\sigma})],\end{split}$

where, given a sampled $\bm{\hat{\theta}}$, the latent variable $z_{n}$ can be
integrated out as:

(29)
$\text{log}p(w_{n}|\bm{\beta},\bm{\hat{\theta}})=\text{log}\sum_{z_{n}}\left[p(w_{n}|\bm{\beta_{z_{n}}})p(z_{n}|\bm{\hat{\theta}})\right]=\text{log}(\bm{\hat{\theta}}\cdot\bm{\beta}).$

Then, the objective function of PPM can be finalized as:

(30)
$\mathcal{L}_{PPM}=-\sum_{n=1}^{|V_{bow}|}\text{log}p(w_{n}|\bm{\beta},\bm{\hat{\theta}})+\mathcal{D}_{KL}[q(\bm{\theta}|\bm{d})||p(\bm{\theta}|\bm{\mu},\bm{\sigma})].$

###  4.4. Persona Information Summarizer

In order to summarize the multi-perspective user preference information, we
propose a persona-aware pointer generator network to generate answers
involving knowledge-level and aspect-level user preference as well as copy
words from a preference-aware user vocabulary. In specific, the knowledge-
level user preference is obtained from the Persona History Incorporation (PHI)
module in Section 4.2, while the aspect-level user preference and the
preference-aware user vocabulary are obtained from the Persona Preference
Modeling (PPM) module in Section 4.3.

Figure 6. Persona Information Summarizer

After incorporating the knowledge-level and aspect-level user preference
information, the decoder layer becomes:

(31)
$\displaystyle\bm{H_{h}}=\text{MultiHead}(\bm{H_{f}},\bm{M_{h}},\bm{M_{h}}),$
(32)
$\displaystyle\bm{H_{p}}=\text{MultiHead}(\bm{H_{h}},\bm{M_{t}},\bm{M_{t}}),$
(33) $\displaystyle\bm{O_{dec}}=\text{FFN}(\bm{H_{p}}),$

where $\bm{M_{h}}$ is the concatenated persona history memory vector of all
the user historical content snippets
$\bm{M_{h}}=[\bm{M_{h_{1}}},\bm{M_{h_{2}}},...,\bm{M_{h_{k}}}]\in\mathbb{R}^{kL_{h}\times
d_{h}}$.

Let $y_{t}$ and $\bm{s_{t}}$ be the decoder input and output at the $t$-th
step, and $\bm{M_{q}}=\bm{O_{q}}$ to be the question memory vector for
avoiding the notational clutter. The attention weight for each word in the
question, the concatenated supporting facts, historical content, and the
learned user vocabulary, i.e., $\bm{\alpha^{q}_{t}}$, $\bm{\alpha^{f}_{t}}$,
$\bm{\alpha^{h}_{t}}$, and $\bm{\alpha^{p}_{t}}$, are generated by:

(34)
$\displaystyle\bm{\alpha^{q}_{t}}=\text{softmax}(\bm{\omega_{q}}^{\intercal}\text{tanh}(\bm{W_{q}}\bm{M_{q}}+\bm{W_{qs}}\bm{s_{t}}+\bm{b_{q}})),$
(35)
$\displaystyle\bm{\alpha^{f}_{t}}=\text{softmax}(\bm{\omega_{f}}^{\intercal}\text{tanh}(\bm{W_{f}}\bm{M_{f}}+\bm{W_{fs}}\bm{s_{t}}+\bm{b_{f}})),$
(36)
$\displaystyle\bm{\alpha^{h}_{t}}=\text{softmax}(\bm{\omega_{h}}^{\intercal}\text{tanh}(\bm{W_{h}}\bm{M_{h}}+\bm{W_{hs}}\bm{s_{t}}+\bm{b_{h}})),$
(37)
$\displaystyle\bm{\alpha^{p}_{t}}=\text{softmax}(\bm{\omega_{p}}^{\intercal}\text{tanh}(\bm{W_{p}}\bm{M_{p}}+\bm{W_{ps}}\bm{s_{t}}+\bm{b_{p}})),$

where $\bm{W_{*}}$, $\bm{W_{*s}}$, $\bm{\omega_{*}}$ and $\bm{b_{*}}$
($*\in\\{q,f,h,p\\}$) are parameters to be learned. The attention weights
$\bm{\alpha^{*}_{t}}$ are used to compute context vectors $\bm{c^{*}_{t}}$ as
the probability distribution over the source words:

(38)
$\displaystyle\bm{c^{q}_{t}}=\bm{M_{q}}^{\intercal}\bm{\alpha^{q}_{t}},\quad\bm{c^{f}_{t}}=\bm{M_{f}}^{\intercal}\bm{\alpha^{f}_{t}},$
(39)
$\displaystyle\bm{c^{h}_{t}}=\bm{M_{h}}^{\intercal}\bm{\alpha^{h}_{t}},\quad\bm{c^{p}_{t}}=\bm{M_{p}}^{\intercal}\bm{\alpha^{p}_{t}}.$

Then, we obtain the attention-based probability distribution by indexing the
attention weight of each word in both the question and review to the extended
vocabulary:

(40) $\displaystyle
P_{q}(y_{t})=\sum\nolimits_{i:w_{i}=w}\bm{\alpha^{q_{i}}_{t}},\quad
P_{f}(y_{t})=\sum\nolimits_{i:w_{i}=w}\bm{\alpha^{f_{i}}_{t}},$ (41)
$\displaystyle
P_{h}(y_{t})=\sum\nolimits_{i:w_{i}=w}\bm{\alpha^{h_{i}}_{t}},\quad
P_{p}(y_{t})=\sum\nolimits_{i:w_{i}=w}\bm{\alpha^{p_{i}}_{t}},$

where $P^{q}(y_{t})$, $P^{f}(y_{t})$, $P^{h}(y_{t})$, and $P^{p}(y_{t})$
denote the attention-based probability distribution for words from the
question, supporting fact, historical content, and user vocabulary,
respectively.

The final probability distribution of $y_{t}$ is obtained from five views of
word distributions, which incorporate the basic information from the question
and supporting facts, the external knowledge from user history, and the
preference-based user vocabulary:

(42) $\displaystyle
P_{all}(y_{t})=[P_{v}(y_{t}),P_{q}(y_{t}),P_{f}(y_{t}),P_{h}(y_{t}),P_{p}(y_{t})],$
(43)
$\displaystyle\bm{\gamma}=\text{softmax}(\bm{W_{\gamma}}[\bm{s_{t}}:\bm{c^{q}_{t}}:\bm{c^{f}_{t}}:\bm{c^{h}_{t}}:\bm{c^{p}_{t}}]+\bm{b_{\gamma}}),$
(44) $\displaystyle P(y_{t})=P_{all}(y_{t})^{\intercal}\bm{\gamma},$

where $\bm{W_{\gamma}}$ and $\bm{b_{\gamma}}$ are parameters to be learned,
$\bm{\gamma}$ is the multi-view pointer scalar to determine the weight of each
view of probability distribution. The overview of the Persona Information
Summarizer is depicted in Figure 6.

###  4.5. Training

The proposed method jointly learns latent preference modeling and answer
generation in an end-to-end fashion. Apart from the objective function of PPM
formulated in Equation (30), the objective function of answer generation is
trained to minimize the negative log likelihood:

(45)
$\mathcal{L}_{QA}=-\frac{1}{T}\sum\nolimits^{T}_{t=0}\text{log}P(w^{*}_{t}),$

where $w^{*}_{t}$ is the target word at the $t$-th timestep.

To ensure the diversity of the resulting preference embeddings in PPM, we add
a regularization term to the objective function $\mathcal{L}_{PPM}$ to
differentiate each preference embedding in the global preference embeddings
$\bm{T}$:

(46) $\mathcal{R}=||\bm{T}\cdot\bm{T}^{\top}-\bm{I}||,$

where $\bm{I}$ is the identity matrix. Finally, the overall training objective
of the proposed method is a weighted sum of $\mathcal{L}_{QA}$,
$\mathcal{L}_{PPM}$, and the regularization term $\mathcal{R}$:

(47)
$\mathcal{L}=\mathcal{L}_{QA}+\lambda_{1}\cdot(\mathcal{L}_{PPM}+\lambda_{2}\cdot\mathcal{R}),$

where $\lambda$ are hyper-parameters to balance losses.

##  5\. Experimental Settings

###  5.1. Dataset Collection

We evaluate our model on a public real-world E-Commerce dataset, Amazon
Question/Answer Dataset (Wan and McAuley, 2016), which contains around 1.4
million product-related questions with multiple answers provided by different
users. This QA dataset can be combined with Amazon Product Dataset (Ni et al.,
2019), which preserves product reviews and metadata from Amazon. Besides,
there is another public real-world PQA dataset (Gao et al., 2019) collected
from one of the largest Chinese E-Commerce platforms, namely JD. However, this
dataset does not contain the user information. Hence we are not able to adopt
it for the personalized PQA evaluation. Following previous studies on answer
generation in E-Commerce (Chen et al., 2019b; Gao et al., 2019; Gupta et al.,
2019), we adopt user-written answers as the ground-truth for the experiment.
However, different from these works, which assume that there is only one
ground-truth answer for each question, we need to collect a set of
personalized answers for different users towards the same question. In fact,
the user-written answers can reflect the user preference towards the given
question more precisely and truly. To this end, following previous
review/answer helpfulness prediction studies (Fan et al., 2019a; Chen et al.,
2019d; Zhang et al., 2020c), we regard those answers, which receive more than
two votes as well as more upvotes than downvotes, as a good or helpful answer
to be the ground-truth answer for the answerer and the given question. Then,
we use the User ID of the answerer to fetch his/her historical posted content,
including questions, answers, and reviews, as the personal information.

Since there are a large number of reviews and metadata, including the
description and features, for each product, we need to extract those
supporting facts that contain relevant information for each question. Similar
to Chen et al. (2019b) and Gupta et al. (2019), each supporting fact text is
chunked into snippets of length 50, or to the end of a sentence boundary. Then
for a given question, we adopt BM25 to rank all the supporting fact snippets
of the corresponding product and collect the top 5 relevant supporting fact
snippets for each question as the model input. The same process is conducted
for retrieving the user history that is relevant to the given question.

Each sample in the final dataset contains a question, a reference personal
answer, a set of user history snippets, and a set of supporting fact snippets.
Three categories with the largest number of samples are adopted, namely
Electronics, Home&Kitchen and Sports&Outdoors. For each category, we leave
1,000 instances for validation, 2,000 for testing, and the remaining are used
for training. The statistics of the dataset are presented in Table 2.

###  5.2. Baseline Methods

We first compare our model to state-of-the-art generative PQA models that
exclude personalization:

  * •

S2SAR (Gao et al., 2019). A method incorporates the review information into
Sequence-to-sequence model with attention (S2SA), by concatenating the
question and reviews as the source text.

  * •

RAGE (Chen et al., 2019b). A state-of-the-art review-driven answer generation
method for product-related questions555https://github.com/WHUIR/RAGE.

  * •

TFMR. The basic encoder-decoder architecture described in Section 4.1 combines
the Transformer (Vaswani et al., 2017) and the BiDAF (Seo et al., 2017) for
answer generation.

Table 2. The statistics of datasets Dataset | Electronics | Home&Kitchen | Sports&Outdoors
---|---|---|---
#(Q,A) | 39,478/1K/2K | 29,867/1K/2K | 12,458/1K/2K
Len(Q) | 28.3/25.1/18.4 | 20.0/21.1/18.1 | 18.9/20.3/18.8
Len(A) | 53.5/50.7/48.5 | 46.1/48.9/43.9 | 46.8/48.8/45.5
#User | 35,546/~1K/~2K | 27,691/~1K/~2K | 11,377/~1K/~2K
#History | 33.8/29.1/33.3 | 34.0/38.8/36.2 | 27.7/29.3/25.3

In order to validate the effectiveness of our proposed method on generating
personalized answers, we adapt several latest personalized text generation
methods for comparison as follows:

  * •

Per-S2SA (Zhang et al., 2018) is a persona-based generation baseline that
appends historical UGC to the input sequence. Here we further concatenate the
supporting facts to the input, namely Per-S2SAR.

  * •

Per-CVAE (Song et al., 2019)666https://github.com/vsharecodes/percvae and
PAGen (Wu et al., 2020) are both personalized dialog generation models that
employ conditional VAE to exploit textual user information for generating
diverse responses. We regard the question and supporting facts as the dialog
context and adopt the UGC as the user information to generate personalized
answers.

  * •

Per-TFMR is developed from TFMR by adding the historical UGC to the supporting
facts for incorporating personal information.

  * •

KOBE (Chen et al., 2019c) is a personalized product description generation
model that considers a variety of factors during generation, including product
aspects, user categories, and external knowledge
base777https://github.com/THUDM/KOBE. We employ neural topic modeling method,
GSM (Miao et al., 2017), to cluster users based on the historical UGC, and
regard the supporting facts as the knowledge base.

###  5.3. Evaluation Metrics

We adopt ROUGE F1 (R-1, R-2, R-L) and Embedding-based Similarity (ES) (Liu et
al., 2016), including ES-Extreme, ES-Greedy, and ES-Average, as evaluation
metrics to measure the performance of answer generation. In addition,
following previous studies on personalized text generation (Song et al., 2019;
Wu et al., 2020), Persona Coverage ($\mathcal{C}_{per}$), Users-Distinct
($uDist$), and User-Language-Perplexity ($uPPL$) are adopted for evaluating
the ability of personalization:

  * •

Persona Coverage ($\mathcal{C}_{per}$) (Song et al., 2019) is adopted to
evaluate how well the personal information is reflected in the generated
answers at word-level. Suppose that there are $N$ samples with $M$ retrieved
personal history snippets for each. The persona coverage score is defined as:

(48)
$\mathcal{C}_{per}=\frac{\sum\nolimits^{N}_{i}\max_{j\in[1,M]}\mathcal{S}(y_{i},p^{i}_{j})}{N},$

where $\mathcal{S}(y_{i},p^{i}_{j})$ is the mean of the inverse document
frequency of shared words $W$ between the generated answer $y_{i}$ and the
personal history snippet $p^{i}_{j}$:

(49) $\mathcal{S}(y_{i},p^{i}_{j})=\frac{\sum\nolimits_{w_{k}\in
W}idf_{k}}{|W|}.$

  * •

Users-Distinct ($uDist$) (Wu et al., 2020) is adopted to measure the in-group
diversity of answers generated within a group of users for the same question.

  * •

User-Language-Perplexity ($uPPL$) (Wu et al., 2020) is used to quantify the
power of a personalized text generation method on learning the user language
style. It adopts a bi-gram language model for perplexity computation, which is
pre-trained with the whole training data and then fine-tuned with each user’s
history content.

###  5.4. Implementation Details

Following the general Transformer settings (Vaswani et al., 2017), we apply a
six-layer Transformer encoder and a two-layer Transformer decoder for all
transformer-based models. The embedding size of the input and the hidden size
is set to be 512. The size of the Transformer FFN inner representation size is
set to be 2048, and ReLU is used as the activation function. The learning rate
and the dropout rate are set to be 0.0001 and 0.1, respectively. During
training, the batch size is set to be 32, while at the inference phase, we use
beam search with a beam size of 10. For each model, we all train for 20
epochs. Based on some preliminary experiments, we find that there is not much
difference on the performance when tuning the cluster number within
{5,10,20,40}. Thus, the cluster number of user preference is set to be 10 for
both KOBE and our proposed model. The hyper-parameter $\lambda$ is set to 1
and 0.1 for balancing losses and the regularization term.

##  6\. Experimental Results

###  6.1. Research Questions

The empirical analysis targets the following research questions:

  * •

RQ1: How is the performance of the proposed method on answer generation,
compared to existing methods? Can answer generation in PQA benefit from
personalization?

  * •

RQ2: How well does the proposed method generate personalized answers in terms
of multi-perspective user preferences?

  * •

RQ3: How does each component in the proposed method contribute to the overall
performance?

  * •

RQ4: Which aspects of product question answering can be improved by
personalization?

###  6.2. Evaluation on Answer Generation (RQ1)

Table 3.  Method comparisons of Answer Generation on Electronics dataset. † indicates that the model is better than the best performance of baseline methods (underline scores) with statistical significance (measured by significance test at $p<0.05$). Model | R-1 | R-2 | R-L | ES-Ext. | ES-Gre. | ES-Ave.
---|---|---|---|---|---|---
BM25 | 13.2 | 1.8 | 11.7 | 35.4 | 66.6 | 84.7
S2SAR (Gao et al., 2019) | 13.5 | 2.3 | 12.5 | 35.8 | 65.6 | 78.9
RAGE (Chen et al., 2019b) | 13.2 | 2.1 | 12.0 | 35.6 | 65.5 | 80.2
TFMR (Nishida et al., 2019) | 14.8 | 2.5 | 13.2 | 36.1 | 65.1 | 85.2
Per-S2SAR (Zhang et al., 2018) | 14.0 | 2.5 | 13.0 | 35.6 | 65.5 | 82.6
Per-CVAE (Song et al., 2019) | 13.3 | 2.0 | 11.9 | 35.1 | 63.4 | 85.0
PAGen (Wu et al., 2020) | 13.5 | 2.1 | 12.0 | 35.5 | 64.4 | 84.6
Per-TFMR | 15.1 | 2.7 | 13.5 | 36.3 | 66.9 | 85.0
KOBE (Chen et al., 2019c) | 15.0 | 2.5 | 13.3 | 36.8 | 67.4 | 84.9
PAGE | 16.9$\dagger$ | 3.5$\dagger$ | 15.1$\dagger$ | 38.3$\dagger$ | 67.7$\dagger$ | 85.0
\- w/o PHI | 16.2 | 3.3 | 14.5 | 37.5 | 67.1 | 85.1
\- w/o PPM | 16.3 | 3.5 | 14.6 | 38.6 | 67.9 | 85.3
\- w/o PIS | 15.7 | 2.8 | 13.9 | 37.1 | 66.6 | 85.2

We first summarize the evaluation results on answer generation performance in
Table 3, 4, and 5. In general, our proposed method, PAGE, consistently and
substantially outperforms all these strong baselines across three different
domains, not only the general answer generation models for PQA, but also the
adapted personalized text generation models. Specifically, there are several
notable observations as follows:

(1) The retrieved fact snippets obtained by BM25 are far from satisfying as an
answer to the given question.

(2) There is an obvious gap between non-persona models and their persona-aware
variants. For instance, Per-S2SAR and Per-TFMR achieve about 1% improvement by
incorporating user information into S2SAR and TFMR. The results validate the
effectiveness of taking into account the user information for answer
generation in E-Commerce scenario.

(3) The results also show that, compared to RNN-based models (e.g., S2SAR,
RAGE, Per-S2SAR, Per-CVAE), Transformer-based models (e.g., TFMR, Per-TFMR,
KOBE) improve the natural language generation performance to a great extent.

(4) Among those persona-aware generation baselines, Per-TFMR achieves the best
performance on content preservation. However, KOBE has a similar performance
with TFMR, which indicates that only using user category feature is inadequate
to improve the answer generation for product-related questions, compared to
introducing personal history as external knowledge.

(5) Finally, our proposed method, PAGE, outperforms all the baselines by a
noticeable margin across three product categories. The results on answer
generation show that PAGE can guarantee the quality of generated answers when
exploring the personalization in PQA.

Overall, this experiment provides the answer to RQ1: (i) The proposed method
substantially and consistently outperforms existing methods on the evaluation
of answer generation. (ii) Answer generation in PQA can benefit from certain
personalization techniques.

Table 4.  Method comparisons of Answer Generation on Home&Kitchen dataset. † indicates that the model is better than the best performance of baseline methods (underline scores) with statistical significance (measured by significance test at $p<0.05$). Model | R-1 | R-2 | R-L | ES-Ext. | ES-Gre. | ES-Ave.
---|---|---|---|---|---|---
BM25 | 12.4 | 2.0 | 12.1 | 36.4 | 66.6 | 85.7
S2SAR (Gao et al., 2019) | 12.2 | 2.0 | 11.4 | 37.1 | 66.6 | 85.1
RAGE (Chen et al., 2019b) | 13.0 | 2.4 | 12.0 | 37.2 | 66.8 | 85.5
TFMR (Nishida et al., 2019) | 15.5 | 2.7 | 13.9 | 37.2 | 66.9 | 85.8
Per-S2SAR (Zhang et al., 2018) | 14.6 | 2.7 | 13.4 | 37.1 | 66.4 | 84.1
Per-CVAE (Song et al., 2019) | 14.8 | 2.7 | 13.9 | 33.9 | 67.0 | 84.6
PAGen (Wu et al., 2020) | 15.3 | 2.9 | 14.1 | 35.9 | 66.8 | 85.2
Per-TFMR | 16.1 | 3.0 | 14.4 | 38.0 | 67.1 | 86.2
KOBE (Chen et al., 2019c) | 16.0 | 2.8 | 14.5 | 37.0 | 66.8 | 85.7
PAGE | 17.8$\dagger$ | 3.9$\dagger$ | 16.0$\dagger$ | 40.6$\dagger$ | 68.8$\dagger$ | 86.5$\dagger$
\- w/o PHI | 17.5 | 3.6 | 15.6 | 39.5 | 68.3 | 86.5
\- w/o PPM | 18.0 | 3.8 | 16.2 | 40.1 | 68.5 | 86.3
\- w/o PIS | 16.9 | 3.3 | 14.8 | 38.2 | 66.9 | 86.0
Table 5.  Method comparisons of Answer Generation on Sports&Outdoors dataset. † indicates that the model is better than the best performance of baseline methods (underline scores) with statistical significance (measured by significance test at $p<0.05$). Model | R-1 | R-2 | R-L | ES-Ext. | ES-Gre. | ES-Ave.
---|---|---|---|---|---|---
BM25 | 13.0 | 1.9 | 11.8 | 35.8 | 66.1 | 85.6
S2SAR (Gao et al., 2019) | 13.4 | 1.9 | 12.5 | 37.1 | 66.6 | 84.7
RAGE (Chen et al., 2019b) | 13.5 | 1.9 | 12.5 | 35.6 | 65.9 | 85.2
TFMR (Nishida et al., 2019) | 13.9 | 2.5 | 12.7 | 34.8 | 65.5 | 85.3
Per-S2SAR (Zhang et al., 2018) | 13.7 | 2.3 | 12.7 | 36.0 | 65.6 | 84.9
Per-CVAE (Song et al., 2019) | 11.9 | 1.9 | 10.4 | 32.9 | 65.6 | 84.5
PAGen (Wu et al., 2020) | 13.9 | 2.3 | 12.6 | 35.6 | 66.5 | 84.9
Per-TFMR | 14.7 | 3.3 | 13.4 | 36.7 | 67.1 | 85.1
KOBE (Chen et al., 2019c) | 13.9 | 2.3 | 13.0 | 36.7 | 66.2 | 85.7
PAGE | 17.4$\dagger$ | 4.3$\dagger$ | 15.8$\dagger$ | 39.3$\dagger$ | 68.6$\dagger$ | 86.4$\dagger$
\- w/o PHI | 16.9 | 3.1 | 15.0 | 39.2 | 68.8 | 87.0
\- w/o PPM | 17.0 | 4.2 | 15.5 | 39.4 | 68.5 | 86.5
\- w/o PIS | 14.4 | 3.4 | 13.8 | 37.2 | 67.1 | 85.8
Table 6.  Method comparisons of Personalization on Electronics dataset. The higher$\uparrow$ (or lower$\downarrow$), the better. We only compare the user perplexity ($uPPL$) among Transformer-based models, since the language probability distribution is different from RNN-based models and ORACLE answers so that their $uPPL$ scores are marked as ”-”. † indicates that the model is better than the best performance of baseline methods (underline scores) with statistical significance (measured by significance test at $p<0.05$).  Model |  $\mathcal{C}_{per}$$\uparrow$ |  $uDist$-1$\uparrow$ |  $uDist$-2$\uparrow$ |  $uPPL$$\downarrow$
---|---|---|---|---
TFMR (Vaswani et al., 2017) | 0.0544 | 0.6205 | 0.7194 | 390.0
Per-S2SAR (Zhang et al., 2018) | 0.0372 | 0.3832 | 0.4967 | -
Per-CVAE (Song et al., 2019) | 0.0248 | 0.5846 | 0.7058 | -
PAGen (Wu et al., 2020) | 0.0359 | 0.5456 | 0.6674 | -
Per-TFMR | 0.0552 | 0.6925 | 0.8583 | 377.5
KOBE (Chen et al., 2019c) | 0.0608 | 0.6449 | 0.7645 | 381.9
ORACLE | 0.2121 | 0.7300 | 0.9481 | -
PAGE | 0.1320$\dagger$ | 0.7229$\dagger$ | 0.8990$\dagger$ | 337.1$\dagger$
\- w/o PHI | 0.0935 | 0.7031 | 0.8507 | 321.2
\- w/o PPM | 0.1299 | 0.7003 | 0.8551 | 372.8
\- w/o PIS | 0.0690 | 0.7326 | 0.8876 | 361.7

###  6.3. Evaluation on Personalization (RQ2)

The evaluation results of personalization on generated answers are given in
Table 6, 7, and 8. It is observed that our proposed method makes a remarkable
performance on generating personalized answers according to the evaluation
metrics of personalized text generation. Note that ORACLE represents the
evaluation results of ground-truth/reference answers. Although TFMR achieves
the best performance on answer generation among non-persona methods, compared
to ORACLE, the performance of personalization is far from ideal. On the other
hand, the ORACLE results on $\mathcal{C}_{per}$ show that the user-generated
answers share certain personal information with the historical UGC whether in
knowledge-, aspect-, or vocabulary-level, which explicitly reflects the
corresponding user preference towards the given question. Meanwhile, the
ORACLE results on $uDist$ indicate the diversity of answers as well as user
preference among different users towards the same question.

For those RNN-based text generation methods (Per-S2SAR & Per-CVAE), the
repetition issue (See et al., 2017) leads to relatively low quality in the
generated answers. Thus, we mainly discuss the performance comparisons among
Transformer-based models. Per-TFMR and KOBE generally achieve better
performance on personalization than TFMR by taking into account user personal
information. In specific, their improvement on persona coverage and user
perplexity metrics is relatively trivial, while Per-TFMR significantly
improves the diversity of the generated answers than KOBE, indicating that
individual user modeling can bring more diversity to generated answers than
group-based user modeling. As for our proposed model, we observe that it makes
substantial improvements on these personalization metrics across different
product domains. First, the performance boosting on $\mathcal{C}_{per}$
indicates that PAGE effectively reflects personal information in the generated
answers. Second, the results on $uDist$ are very closed to the ORACLE results,
which shows the promising diversity of the generated answers by PAGE. Third,
the improvement on $uPPL$ shows that PAGE can generate answers more closely
with user-preferred language styles.

This evaluation on personalization-related metrics provides the answer to RQ2:
The proposed method effectively generates personalized answers with a higher
diversity of user-centric information as well as user-preferred language
styles.

Table 7.  Method comparisons of Personalization on Home&Kitchen dataset. The higher$\uparrow$ (or lower$\downarrow$), the better. We only compare the user perplexity ($uPPL$) among Transformer-based models, since the language probability distribution is different from RNN-based models and ORACLE answers so that their $uPPL$ scores are marked as ”-”. † indicates that the model is better than the best performance of baseline methods (underline scores) with statistical significance (measured by significance test at $p<0.05$). Model |  $\mathcal{C}_{per}$$\uparrow$ |  $uDist$-1$\uparrow$ |  $uDist$-2$\uparrow$ |  $uPPL$$\downarrow$
---|---|---|---|---
TFMR (Vaswani et al., 2017) | 0.0567 | 0.5952 | 0.6827 | 319.0
Per-S2SAR (Zhang et al., 2018) | 0.0395 | 0.3756 | 0.4862 | -
Per-CVAE (Song et al., 2019) | 0.0268 | 0.5380 | 0.6696 | -
PAGen (Wu et al., 2020) | 0.0326 | 0.5008 | 0.6327 | -
Per-TFMR | 0.0578 | 0.6654 | 0.8498 | 312.3
KOBE (Chen et al., 2019c) | 0.0640 | 0.6081 | 0.7096 | 374.0
ORACLE | 0.2013 | 0.7170 | 0.9465 | -
PAGE | 0.1342$\dagger$ | 0.7027$\dagger$ | 0.8769$\dagger$ | 296.1$\dagger$
\- w/o PHI | 0.1159 | 0.6409 | 0.8254 | 300.3
\- w/o PPM | 0.1327 | 0.6634 | 0.8508 | 315.1
\- w/o PIS | 0.0651 | 0.6978 | 0.8675 | 298.1
Table 8.  Method comparisons of Personalization on Sports&Outdoors dataset. The higher$\uparrow$ (or lower$\downarrow$), the better. We only compare the user perplexity ($uPPL$) among Transformer-based models, since the language probability distribution is different from RNN-based models and ORACLE answers so that their $uPPL$ scores are marked as ”-”. † indicates that the model is better than the best performance of baseline methods (underline scores) with statistical significance (measured by significance test at $p<0.05$).  Model |  $\mathcal{C}_{per}$$\uparrow$ |  $uDist$-1$\uparrow$ |  $uDist$-2$\uparrow$ |  $uPPL$$\downarrow$
---|---|---|---|---
TFMR (Vaswani et al., 2017) | 0.0374 | 0.6215 | 0.7071 | 331.5
Per-S2SAR (Zhang et al., 2018) | 0.0213 | 0.3490 | 0.4397 | -
Per-CVAE (Song et al., 2019) | 0.0172 | 0.6343 | 0.6764 | -
PAGen (Wu et al., 2020) | 0.0224 | 0.5994 | 0.6412 | -
Per-TFMR | 0.0391 | 0.7090 | 0.8675 | 310.0
KOBE (Chen et al., 2019c) | 0.0347 | 0.6713 | 0.7326 | 334.6
ORACLE | 0.2298 | 0.7312 | 0.9519 | -
PAGE | 0.1448$\dagger$ | 0.7367$\dagger$ | 0.8839$\dagger$ | 284.7$\dagger$
\- w/o PHI | 0.0967 | 0.7209 | 0.8697 | 261.8
\- w/o PPM | 0.1190 | 0.7062 | 0.8692 | 304.2
\- w/o PIS | 0.0778 | 0.7382 | 0.8738 | 306.2

###  6.4. Effectiveness of Components (RQ3)

####  6.4.1. Ablation Study

Since we propose three specific modules, including Persona History
Incorporation (PHI), Persona Preference Modeling (PPM), and Personal
Information Summarizer (PIS), for modeling personal preference from different
perspectives, we conduct ablation study to testify the importance of these
modules in answer generation and personalization. In specific, “w/o PPM” means
we only take the retrieved historical UGC $\mathcal{H^{*}}$ as the additional
personal information for PHI module to capture the knowledge-level user
preferences, while the PPM module is non-functional. “w/o PHI” means we only
take the BoW representation of $\mathcal{H^{*}}$ as the additional personal
information for PPM module to capture the aspect-level and vocabulary-level
user preferences, while the PHI module is disabled. “w/o PIS” means we take
the original vocabulary distribution of the Transformer decoder to generate
the answers without the consideration of the user vocabulary and the words in
the source text.

As for the content-preserving metrics in Table 3, 4, and 5, it can be observed
that PIS module contributes to the most significant improvement to the final
performance, which is attributed to two main reasons. First, in accordance
with the findings in Section 6.3, people are likely to talk with their own
knowledge or wording habits when writing answers. Second, in fact, PIS module
serves as a summarizer to integrate all the learned personal information,
rather than a simple component. Comparatively, discarding PHI or PPM modules
makes not much difference in the content-preserving performance. We conjecture
that these two modules can serve as a complementary component to fetch
personal information for the PIS module to generate persona-aware answers (one
from knowledge-level, the other from aspect-level and vocabulary-level).

As for the personalization metrics in Table 6, 7, and 8, we can find that both
PHI and PPM modules contribute to the diversity of the generated answers among
different users, e.g., $uDist$. This indicates that the answers to the same
question actually vary in terms of user preferences towards different product
aspects or practical knowledge from person to person. Separately, PHI module
successfully incorporates user history into the generated answers as shown in
the $\mathcal{C}_{per}$ metric, while PPM module is useful for generating
answers that are more consistent with the user’s language style, as reflected
by $uPPL$. The results demonstrate the effectiveness of PHI and PPM modules on
incorporating personal knowledge and building user-specific vocabulary.
Conversely, PIS module makes little contribution to diversifying the generated
answers, but it guarantees the model to effectively leverage user historical
information and preference-based user vocabulary for answer generation.

Overall, the multi-perspective preference modeling contributes to the
personalization of PQA from different perspectives and aids in generating
answers with remarkable content quality, which is the answer to the RQ3.

####  6.4.2. Latent Aspect-level Preference Clustering

The Persona Preference Modeling module is proposed to extract latent aspect-
level preference features from user history for guiding personalized answer
generation. To facilitate further investigation of the aspect-level preference
modeling, we derive the semantic similarity matrix $\beta$ from Equation 24 in
each category, and rank the top representative words for each preference
cluster. The results of clusters and words for Electronics and Home&Kitchen
categories are presented in Table 9. Note that the preference type of each
cluster is inferred from the cluster results, since there is no ground-truth
label for the latent cluster. From the personal preference modeling result, we
observe that the PPM module effectively cluster users into different
preference categories, with different representative words. There are obvious
inclinations for each cluster in different product domains. In the Electronics
category, user-preferred aspects are clustered into different groups, such as
Camera, Sound, and Device Configuration, etc. For instance, some users may
prefer to know about the device configuration of certain electronic products,
while some may care more about the photography related information. In the
Home&Kitchen category, it can be observed that there are some users who prefer
to Drink&Food, Cooker&Material, or Size&Quality. Meanwhile, there are some
preference clusters shared across different categories, e.g. Price. The
clustering results answer RQ3 from a different angle: On one hand, personal
preference modeling can provide aspect-level user preference information as
well as assist in building a preference-aware user vocabulary, which
contributes to the personalization of answer generation. On the other hand,
such latent topic modeling approach provides an unsupervised way to discover
the latent features of different users based on their history content.

Table 9. Top representative words for each aspect-level preference cluster on two different product categories Category |  Preference 1 |  Preference 2 |  Preference 3 |  Preference 4 | $\dots$
---|---|---|---|---|---
|  Camera |  Sound |  Configuration |  Price | $\dots$
Electronics |  Camera, Lens, Canon, Nikon, Flash, Zoom, Sony, Focus, Pictures |  Sound, TV, Quality, Cable, Speakers, Radio, Audio, Headphones |  USB, Drive, Port, Windows, Device, Computer, Software, Player, HDMI |  Product, Price, Nice, Bought, Buy, Recommend, Bit, Purchase, Money
|  Drink&Food |  Cooker&Material |  Size&Quality |  Price | $\dots$
Home& Kitchen |  Coffee, Water, Pot, Cup, Tea, Easy, Hot, Food, Bread, Rice, Milk, Taste |  Pan, Machine, Oven, Cooker, Maker, Stainless, Plastic, Steel, Cooking |  Size, Fit, Nice, Quality, Perfect, Comfortable, Sturdy, Hold, Heavy |  Unit, Product, Bought, Buy, Price, Worth, Purchase, Recommend, Money

###  6.5. Discussions (RQ4)

####  6.5.1. Human Evaluation

To further evaluate the quality of generated answers from each model, we also
conduct human evaluation to evaluate the generated answers by scoring the
following six aspects:

  * •

Informativeness: how rich is the generated answer in information towards the
given question?

  * •

Fluency: how fluent is the generated answer?

  * •

Relevancy: : how relevant is the generated answer to the given question?

  * •

Diversity: how diverse are the generated answers for different users towards
the same question?

  * •

Persona: how well does the generated answer reflect the user’s personal
preference?

  * •

Overall Quality: : how helpful is the generated answer to the user according
to the overall quality?

We randomly sample 50 instances from each category and generate their answers
with four methods, including TFMR, Per-TFMR, KOBE, and the proposed PAGE.
Three annotators are recruited to score each generated answer with 1 to 5
(higher the better). These annotators are all well-educated research
assistants with a related background of NLP. However, since the degree of
Persona is difficult to be quantified by human judgment, we simplify the
scoring into a binary score, representing the judgment of whether the
generated answer can reflect the user’s persona.

Table 10 summarizes the results of human evaluation. It can be observed that
our model, PAGE, consistently outperforms these strong baselines from
different aspects of human evaluation. It is noteworthy that the
Informativeness, Diversity, and Persona are significantly improved by PAGE,
which demonstrates that PAGE effectively enriches and diversifies the
generated answers by taking into account user preference. According to the
scores of Overall Quality, which measures the quality of the generated answers
against the reference answers, it is beneficial to exploit personalization for
product question answering in E-Commerce.

The human judgements provide the answer to the RQ4: The answers generated by
the proposed method preserve a higher degree of informativeness, diversity as
well as explicitly reflect some user-centric persona, which contributes to a
higher overall quality.

Table 10. Human evaluation results. The Fleiss’ kappa of the annotations is 0.69, which indicates “substantial agreement”, and the final scores are calculated by average. Method | Informativeness | Fluency | Relevancy | Diversity | Persona | Overall Quality
---|---|---|---|---|---|---
TFMR | 2.78 | 3.93 | 3.42 | 1.00 | 1.7% | 1.90
Per-TFMR | 3.02 | 3.97 | 3.53 | 3.15 | 7.2% | 2.17
KOBE | 2.85 | 3.68 | 3.18 | 1.75 | 5.0% | 1.98
PAGE | 3.30 | 4.02 | 3.57 | 3.70 | 15.8% | 2.78

####  6.5.2. Case Study

Table 11. Case study Question |  While a TV is mounted, can you also mount a sound bar underneath the TV?
---|---
Supporting Fact Snippets (Partial) |  1\. Whats also nice is that I have a sound bar, so I just simply placed it on the top shelf of the stand and it sounds as if the audio is coming right through the TV!
2\. It is sturdy, holds a 50 inch TV and swivels with no problems.
3\. I have my Viera 55”; mounted on this and it is perfect!
UGC (User1) |  I ’ve used these to mount a pair of Polk Audio RTI A1 speakers, … // … so they most definantly will hold a 12 pound speaker. // Peerless is known in the audio world for making quality products.
Reference1 |  Yes . The TV can be mounted on a pedestal that is attached to the back of the stand . This allows you to use the top shelf for a soundbar or center channel speaker.
TFMR |  Yes it can be mounted on the wall.
Per-TFMR |  Yes, the TV can be mounted on either the left or right side.
KOBE |  Yes, the TV can be removed from the floor. The stand is designed so that the stand will stand up against the wall.
PAGE |  Yes. The TV can be mounted on a pedestal that is attached to the top of a cabinet. It is designed to sit on a horizontal TV speaker and secured into the TV.
UGC (User2) |  This TV stand is perfect for our new 50” SONY TV that we bought from Amazon at the same time.
Reference2 |  With our 50” Sony mounted on the lowest position on the mounting bars, I have 6-3/8” from the top glass shelf to the bottom of the TV frame. If you move the TV up to the top of the mounting bars you would have about 12” of space.
TFMR |  Yes it can be mounted on the wall.
Per-TFMR |  Yes. The TV can be mounted on either side or left or right or left. There is a mounting bracket on the back of the TV.
KOBE |  Yes, the TV can be adjusted vertically and the sides of the TV will stand up from the floor. It is a great value for the price.
PAGE |  Yes. We have a 50 inch TV (slides left to right) and it fits perfectly.
UGC (User3) |  Quality of the unit was excellent and sturdy. I mounted my 55” LED Sony TV to the stand. … // I don’t see why it couldn’t be used for other bake goods. …
Reference3 |  The TV I have is a 55 inch. I use a stereo for surround sound, but I have a 5 inch center speaker under the TV.
TFMR |  Yes it can be mounted on the wall.
Per-TFMR |  It can be mounted on the back of the TV. The stand is on the top of the TV. There is no way to mount it to the wall.
KOBE |  Yes, the TV can be removed from the floor. The stand is designed so that the stand will stand up against the wall.
PAGE |  I don’t see why not. I have a 55 inch TV. I put it on the TV and it works great.

In order to intuitively differentiate our proposed method from other
baselines, Table 11 presents a specific product-related question from Amazon
QA. Even though the question is asking for a YES or NO answer, different
former buyers still provide diverse responses (Reference) reflecting their own
preferences. Besides, it can also be observed that the user-generated answers
(Reference) typically share some similar preferences with their historical
UGC. For instance, User1 has a strong preference towards the product aspect of
sound quality, which is reflected in both his/her historical posted content
and his/her answer to the given question, while User2 and User3 tend to share
their knowledge from previous shopping experiences, which have already been
shared in reviews before.

For the baseline methods, non-persona methods (e.g., TFMR) generate a unified
answer for all individual users, which only involves the basic information
obtained from the common opinion of supporting facts. Users’ personal
information enables personalized text generation methods (e.g., Per-TFMR and
KOBE) to provide answers with additional information for different users
accordingly. Among them, the answers generated by Per-TFMR are more diverse
than those generated by KOBE. KOBE only captures group-based preference, as it
models user features by classifying users into several pre-defined categories,
while Per-TFMR employs historical UGC as textual personal information for
individual user modeling. However, both of them fail to explicitly incorporate
user preference into the generated answers. That is to say, although they
improve the diversity of the generated answers for different users, the
personalization is not actually reflected in the answers.

With multi-perspective preference modeling, PAGE effectively generates
informative and customized answers involving specific user preferences.
Similar to the reference answers, PAGE provides personalized answers for User1
reflecting his/her aspect-level preference towards sound equipment. As for
User2 and User3, the generated answers are combined with the practical
knowledge from supporting facts for satisfying their individual information
needs. Besides, we notice that PAGE can capture some vocabulary-level
preference or preferred language style, e.g., User2 likes to use “perfect” to
comment on the product, while User3 prefer to start talking with “I don’t see
why”. Overall, the case study provides a piece of intuitive evidence to the
RQ4.

##  7\. Conclusions and Future Work

In this paper, we investigate the problem of personalized answer generation in
E-Commerce, which aims at exploring personal information from historical user-
generated content to aid in generating personalized answers to product-related
questions. Specifically, we propose a novel personalized answer generation
method with multi-perspective preference modeling to comprehensively capture
user preference for generating persona-aware answers. A persona-aware pointer-
generator network, which bridges neural topic modeling and pointer network, is
developed to dynamically learn the user preference and the user-specific
vocabulary for generating persona-aware answers. The proposed method
outperforms existing methods on real-world E-Commerce QA datasets and
effectively generates informative and customized answers for different users
by incorporating various user preferences from the perspective of knowledge-,
aspect-, and vocabulary-level. In addition, the empirical analysis shows that
answer generation in E-Commerce can benefit from personalization.

This work is the first attempt towards personalized answer generation for PQA.
There are some limitations and room for further improvement. For example,
besides historical UGC, there are other types of user data for user modeling
in E-Commerce, such as user-item interaction. It can be beneficial to leverage
such heterogeneous or multi-modal data for better user preference modeling.
Similar to recent personalized justification or explanation generation studies
for item recommendation, another future direction can be to develop graph-
based reasoning methods for building explainable PQA systems with
personalization. Besides, the personalized answer generation study can be
extended to other personalized text generation applications in E-Commerce, or
other user-centric QA forums, such as Stack Overflow, etc.

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