A Book Recommendation SystemConsidering Contents and Emotions ofUser Interests
Takumi Fujimoto, Harumi Murakami
Conference

2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)

Date of Conference2022-07-02_08

TypeConference paper

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DOI

10.1109/IIAIAAI55812.2022.00039

Self-Archiving by Author(s)Fujimoto, T., & Murakami, H. (2022). A Book Recommendation System Considering Contents andEmotions of User Interests. In 2022 12th International Congress on Advanced Applied Informatics(IIAI-AAI). 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE.
https://doi.org/10.1109/iiaiaai55812.2022.00039

A Book Recommendation System Considering
Contents and Emotions of User Interests
Takumi Fujimoto
Faculty of Engineering
Osaka City University
Osaka, Japan
Email: fujimoto.takumi.68h@st.kyoto-u.ac.jp

Harumi Murakami
Graduate School of Informatics
Osaka Metropolitan University
Osaka, Japan
Email: harumi@omu.ac.jp

Abstract— Although the benefits of reading are widely
recognized, many people seldom read even though they often claim
to have interest in reading. Since conventional book
recommendation systems require keywords or a browsing history
related to books that reflect user interests, users who rarely readstruggle to obtain satisfactory results. In this study, we propose abook recommendation system that enables both users who read
habitually and those who rarely read to easily get results thatreflect their interests with their own content of interest as queries.
Our proposed method identifies recommended books based on thesimilarity of the vectors of contents and emotions, contained in
tweets about the content of user interests and book reviews. In thisstudy’s experiments, we confirmed the effectiveness of ourproposed method.

users who seldom read. In our system, the content of the interestof users is wielded as a query, and book recommendations arebased on the similarity of contents and emotions in the reviewsof others from various social media sources.

Keywords—book
Twitterrecommendation,sentiment

analysis,I. INTRODUCTION
Many studies show the benefits of reading. Research on
book recommendation systems [1] has identified such benefits
as increased life satisfaction, improved empathy and social
support skills, and better social communication. A study on therelationship between reading and health [2] concluded thatpeople, who read more than 3.5 hours a week, have a 23% lower
risk of death than non-readers. According to a survey by theAgency for Cultural Affairs, Government of Japan [3], of almost
3,600, 47.3% answered that they did not read a book a month.
On the other hand, 60.4% answered “strongly agree” or “agree”
to the question “Do you want to read more?” This result suggeststhat many people are interested in reading, even though they do
not actually read very much. Perhaps one explanation is thatthose who rarely read struggle to get book recommendation
results that reflect their interests.
In previous book recommendation systems, the suggested
books are generally determined based on keywords andbrowsing history related to books. Unfortunately, non-readers
might not get satisfactory recommendation results because theylack keywords or adequate browsing histories that reflect theirown interests.
In this study, we propose a book recommendation systemthat simply obtains recommendation results that reflect theinterests of both users who have regular reading habits and those

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II. RELATED WORKThe following algorithms are commonly used inrecommender systems: content-based filtering (CB),collaborative filtering (CF), and hybrid systems that combine
these two.
Content-based filtering determines items to berecommended using the characteristics of book genres andreviews. Many studies have investigated accurate book
recommendations with CB, improving performance throughiterative machine learning and user evaluations ofrecommendation results based on book titles and themes [4] andsuggesting books based on the similarity of such elements as
subjects and covers [5]. However, CB suffers from a bias towarditems with similar contents and the inability to provide valuable
recommendations when it lacks sufficient information.
Collaborative filtering determines which items to recommend
by estimating the appropriateness of unevaluated items with thebehavioral histories of users. Various studies have addressedbook recommendation systems with CF, including proposing
similar items based on such behavior history as purchases andevaluations [6], modeling preferences based on user access logs
and making recommendations based on other similar users [7],and combining CF systems on each of the book and author
characteristics [8]. One drawback of the CF approach is that itcannot provide valuable recommendations when sufficient data
are unavailable, such as new items or new users. Therefore,
many studies on hybrid systems have combined CB and CF tocomplement their respective shortcomings [9, 10, 11].
Some investigations of recommender systems improve theaccuracy of their suggestions by considering the emotions that
items illicit in users. A study on movie recommendations [12]addressed both a film’s characteristics and the emotions
contained in reviews and summaries to determine suggestions
and consistently improved recommendation accuracy byconsidering the emotions evoked by movies. In book
recommendation studies, several studies improved the accuracy

of book retrieval and recommendation by focusing on theemotional words in book reviews [13, 14].
Our study differs from previous studies using CB
considering the emotions evoked by books in readers and caneasily be used even by non-readers based on query contents
comprised of their own interests.
III. METHOD
An overview of our proposed method is shown in Fig. 1.
Book recommendations are determined by the calculated scoresof each book based on the similarity of the contents and thesentiment between a tweet set on the content in which a user hasinterest and a review set about the book.

Fig. 1. Overview of proposed method.

A. Getting Book Reviews and Tweets
Reviews on books were acquired using Bookmeter, one of
the largest book review sites in Japan. To calculate a book’s
content and sentiment vectors from a set of book reviews, theacquired reviews must include its contents and emotions.
Therefore, reviews with fewer than 120 characters, half ofBookmeter’s character limit, were excluded. Books with fewer
than 200 reviews were also excluded to prevent bias caused byan insufficient number.
We acquired tweets on content a user is interested in using
Twitter API. To calculate the content and sentiment vectors
from a set of tweets, the acquired reviews must include its
contents and emotions. Therefore, tweets with fewer than 70
characters, half of Twitter’s character limit, were excluded fromthe acquired tweets. In addition, content with fewer than 100
tweets was excluded to prevent bias caused by an insufficientnumber.
B. Content Similarity
The content vector of each book and content weredetermined by averaging all the vectors of that content or bookcalculated by Sentence-BERT, which represented documents asa 768-dimensional vector. We used Japanese pre-trained
Sentence-BERT model, which was published by Isamu Sonobe
from NS Solutions Corporation. Its evaluation showed thehighest performance using the 5-nearest neighbors algorithm ina study that compared the Sentence-BERT performance withsix other Sentence-BERT models based on existing Japanese
BERT models [15].

We calculated the content similarity between the content
users are interested in and the book by the cosine similarity of
the content vectors of both.
C. Sentiment Similarity
Each book and the content's sentiment vectors weredetermined by averaging all the vectors whose content or bookwere calculated with ML-Ask, which represented the documents
as a 10-dimensional sentiment vector that consisted of joy, anger,sadness, fear, shame, fondness, dislike, excitement, relief, andsurprise.
As shown in Table 1, for the King Gnu (a popular Japaneserock band) query, the emotions of “joy” and “excitement” werestrong due to the effect of songs like boy that encouraged
listeners to be brave and face their problems. For query aboutJoker (a dark movie in 2019), “dislike” was very strong, and“joy” was very weak due to not only a movie contents itself but
also a stabbing incident in Japan that the killer stated that headmired the Joker. Bump of Chicken is a popular Japanese rockband whose songs often include emotions of nostalgia andsentimental. For this query, “sadness” and “fondness” werestrong, and “dislike” was low due to the effect of such songs as
Nanairo and Acacia, which remind listeners of nostalgia andconvey a positive message about overcoming a painful past. The
results in Table 1 show that the sentiment vectors using ML-Ask
reflected the emotions and impressions given by the querycontent.
The sentiment similarity between the user content of interestand books was calculated by the cosine similarity of eachsentiment vector.
TABLE I.

EXAMPLES OF SENTIMENT VECTORS OF USER CONTENT OF
INTERESTKing Gnu

Joker

Bump ofChicken

Joy

2.67

0.54

2.02

Anger-0.72

-0.42

-1.06

Sadness

0.19

-0.13

0.94

Fear-0.58

-0.62

-1.06

Shame-0.86

-0.91

-0.86

Fondness

0.19

0.44

1.10

Dislike

0.19

2.68

0.01

Excitement

0.33

-0.42

0.11

Relief

-0.77

-0.52

-0.65

Surprise

-0.63

-0.62

-0.55

D. Score between User Content of Interest and Each Book
We define score S(c,bi) between the user content of interestc and each book bi as follows:
S (c, bi ) =   content_ sim (c, bi ) + (1 −  )  sentiment _ sim(c, bi ) , (1)where α is a variable between 0 to 1, content_sim(c,bi) is acosine similarity between the content vectors of c and bi, andsentiment(c,bi) is a cosine similarity between the sentimentvectors of c and bi.

IV. PRELIMINARY EXPERIMENT
We conducted a preliminary experiment order to determine
parameter α in (1). In this experiment, 10 university students
aged 21-22 rated each book on a scale of one to five concerning
the following statement: “The item recommended to me matches
my interests.”
In the preliminary experiment results (Fig. 2), α = 0 was theevaluation for the result with just the sentiment vector using MLAsk, and α = 1 is the evaluation for the result with just thecontent vector using Sentence-BERT. The proposed method
performance was always better than only using sentiment
analysis by ML-Ask and the distributed representation by
Sentence-BERT, and the proposed method showed its highest
performance at α = 0.4. For this reason, we decided α = 0.4 in(1).
Table 2 shows examples of the results of the proposed
method and previous book recommendation systems. When thequery was King Gnu, the first, second, and fourth books(Haikyu!! Series, which contained elements such as effort andcommitment to one’s dreams) reflected a song called boy, whose
theme was courage and the power of assertion. The fifth book,which emphasized affection for friends/family and humanconnections, reflected the query represented by a song named
Itizu, which evokes in listeners a pure, single-minded love. The
third book, which focused on the musical genius of characterswith a wide range of musical expressions, reflected manydifferent musical genres like King Gnu. Thus, Table 2 showsthat our proposed method recommended books based on aquery’s content and the emotions and impressions evoked inusers.

complicated. Therefore, we evaluated its performance bycomparing it with the results of a previous system that usedbooks as queries that reflected user interests. As a previous book
recommendation system, we used an Amazon recommendation
called “Popular products inspired by this item.”
TABLE II.

RECOMMENDATION RESULTS WITH KING GNU QUERY

Proposed method

Amazon (1/12/2022)

Bookmeter (1/12/2022)

Haikyu!! Vol. 36

Band score pieceBP2395 Itizu

PIA MUSICCOMPLEX(PMC)
Vol.16

Haikyu!! Vol. 37

Piano piece for easy
playing PPE52
Sakayume

PIA MUSICCOMPLEX(PMC)
Vol.17Honeybees andDistant Thunder

Piano piece for easy
playing PPE51 Itizu

Piano Piece PP1697
Sanmon Syousetsu

Haikyu!! Vol. 33

Piano piece PP1836
Sakayume

SWITCH Vol.39 No.2

March comes inlike a lion Vol. 14

Piano piece PP1835
Itizu

AERA 2020-2/3 issue

A. Experiment Setup
We experimentally evaluated the proposed and comparison
methods with 11 university students aged 21-22. The dataset forthe proposed method consisted of 233,822 reviews of 2,633
books obtained from Bookmeter’s monthly book rankings fromJanuary 2013 to October 2021 in the following four categories:
hardcovers, paperbacks, comics, and light novels. In theproposed method’s evaluation experiment, we evaluated the top
five books determined by each user's content of interest as aquery. We also evaluated the top five books of Amazon'srecommendations that were determined using each user's booksof interest as a query.
We evaluated the performance of each method by revising
the ResQue questionnaire [16], which is a widely usedevaluation framework for recommender systems. Table 3 lists
the evaluation items and questions. Concerning thequestionnaire items, excluding UI-related and redundant items,
six items (Q1~Q3, Q6~Q8) came from ResQue, and we added
two items (Q4, Q5) to the performance evaluation of theproposed method.

Fig. 2. Results of preliminary experiment.

V. EXPERIMENTS
Previous book recommendation systems fail to provide
satisfactory results when the user's content of interest isemployed as queries. For example, Table 2 shows the resultswith the previous systems when King Gnu was used as a query.
The results with the proposed method addressed the band’scontents, their music, and the emotions and impression evokedin users. Since the Amazon and Bookmeter results only
recommended music scores of their songs and weekly and musicmagazines that they are on the cover, evaluating theperformance of the proposed method by comparing it with theresults of previous systems with the same query wasConcerning the evaluation items, Q1~Q5 are 5-point ratingsfor each recommended item, and Q6~Q8 are 5-point ratings forthe method based on all the recommended items.
B. Results and Discussion
The experiment’s results (Fig. 3) shows that the proposed
method outperformed the comparison method in every items
except Diversity (Q6). Intention (Q3), Content reflection (Q4),Sentiment reflection (Q5), and Satisfaction (Q8) weresignificantly superior to the comparison method.
For evaluations of the proposed method, here are examples
of some responses from participants: “I felt that therecommendation was made based on emotions and elements thatare difficult to verbalize as keywords” and “It accurately foundbooks that were related to my interests.” Both comments suggestthat the results using the proposed method reflect the content ofusers’ interest and each book as well as the emotions andimpressions invoked them.
We also obtained several complaints from users that “Booksin the same series appeared in the results” for the proposed
method's Diversity, which was the only evaluation item that waslower than the performance of the comparison method. This
result can probably be attributed to the fact that book reviews
and reviewers tend to be similar in the same series.
Each evaluation item, including Diversity, was improved byconsidering such book elements as title and author when
deciding which books to recommend.
TABLE III.

EVALUATION ITEMS AND EACH QUESTION

ItemQuestion

Q1

Accuracy

The item recommended to me matches
my interests.

Q2

Novelty

The item recommended to me is novel.

Q3

Intention

I would read the item recommended,given the opportunity.

Q4

Content
reflection

The item recommended to me reflects thequery’s content.

Q5

Sentiment
reflection

The item recommended to me reflects
my query’s impression.

Q6

DiversityThe items recommended to me arediverse.

Q7

Usefulness

The recommender helped me find theideal item

Q8

Satisfaction

I am satisfied with this recommender.

Fig. 3. Experiment results.

VI. CONCLUSIONS
We developed a book recommendation system whose results
reflect the interests of both readers and non-readers. We

proposed a method that identified recommended books based on
the similarity between the contents and emotions that areincluded in the content of the users’ interest and books. We
experimentally confirmed our proposed method’s effectivenessby comparing its results with Amazon's recommendation results.
In the future, we will improve the usability of our proposed
method by reviewing it and the conditions of getting tweets
related to user content of interest to increase its flexibility.
REFERENCES[1]

H. Alharthi, D. Inkpen, and S. Szpakowicz, “A survey of book
recommender systems,” in Journal of Intelligent Information Systems
51.1, 2018, pp. 139–160.
[2] A. Bavishi, M. D. Slade, and B. R. Levy, “A chapter a day: Associationof book reading with longevity,” in Social Science & Medicine, Vol. 164,2016, pp. 44–48.
[3] Agency for Cultural Affairs, “Summary of the results of the 2018 Public
Opinion Poll on the Japanese Language,” Public Opinion Poll on JapaneseLanguage, 2018, pp. 8–10.
[4] R. J. Mooney and L. Roy, “Content-based book recommending using
learning for text categorization,” in Proceedings of the fifth ACM
conference on Digital libraries, 2000, pp. 195–204.
[5] M. S. Pera and Y. K. Ng, “Analyzing book-related features to recommend
books for emergent readers,” in Proceedings of the 26th ACM conferenceon hypertext & Social Media, 2015, pp. 221–230.
[6] G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Itemto-item collaborative filtering,” in IEEE Internet Computing, Vol. 7(1),2003, pp. 76–80.
[7] C. Yang, B. Wei, J. Wu, Y. Zhang, and L. Zhang, “CARES: a rankingoriented CADAL recommender system,” in Proceedings of the 9thACM/IEEE-CS joint conference on Digital libraries, 2009, pp. 203–212.
[8] P.C. Vaz, D. Martins de Matos, and B. Martins, “Improving a hybrid
literary book recommendation system through author ranking,” inProceedings of the 12th ACM/IEEE-CS joint conference on digitallibraries, 2012, pp. 387–388.
[9] S. Givon and V. Lavrenko, “Predicting Social-tags for Cold Start Book
Recommendations,” in Proceedings of the third ACM conference on
Recommender systems, 2009, pp. 333–336.
[10] M. Chandak, S. Girase, and D. Mukhopadhyay, “Introducing hybrid
technique for optimization of book recommender system,” in Procedia
Computer Science, Vol. 45, 2015, pp. 23–31.
[11] Z. Ali, S. Khusro, and I. Ullah, “A hybrid book recommender system
based on table of contents (ToC) and association rule mining,” inProceedings of the 10th International Conference on Informatics andSystems, 2016, pp. 68–74.
[12] Y. Moshfeghi, B. Piwowarski, and J.M. Jose, “Handling data sparsity incollaborative filtering using emotion and semantic based features,” inProceedings of the 34th international ACM SIGIR conference on
Research and development in Information Retrieval, 2011, pp. 625–634.
[13] A. Htait, S. Fournier, P. Bellot, L. Azzopardi, and G. Pasi, “Usingsentiment analysis for pseudo-relevance feedback in social book search,”
in Proceedings of the 2020 ACM SIGIR on International Conference onTheory of Information Retrieval, 2020, pp. 29–32.
[14] S.S. Sohail, J. Siddiqui, and R. Ali, “Feature extraction and analysis ofonline reviews for the recommendation of books using opinion miningtechnique,” in Perspectives in Science, Vol. 8, 2016, pp. 754–756.
[15] N. Shibayama and H. Shinnou, “Construction and Evaluation of Japanese
Sentence-BERT Models,” in Proceedings of the 35th Pacific Asia
Conference on Language, Information and Computation, 2021, pp. 529–536.
[16] P. Pu, L. Chen, and R. Hu, “A user-centric evaluation framework forrecommender systems,” in Proceedings of the fifth ACM conference on
Recommender Systems, 2011, pp. 157–164.