Use of Library Loan Records for Book Recommendation
Keita Tsuji†, Erika Kuroo‡, Sho Sato†, Ui Ikeuchi†, Atsushi Ikeuchi†, Fuyuki Yoshikane†
and Hiroshi Itsumura†† Graduate School of Library, Information and Media Studies, University of Tsukuba
1-2 Kasuga, Tsukuba-city, Ibaraki-ken 305-8550, Japan.
{keita|min2fly|oui|atsushi|fuyuki|hits}@slis.tsukuba.ac.jp‡ College of Knowledge and Library Sciences, School of Informatics, University of Tsukuba
1-2 Kasuga, Tsukuba-city, Ibaraki-ken 305-8550, Japan.
s0811586@u.tsukuba.ac.jp
Abstract To show the effectiveness or limitation ofusing library loan records for book recommendation,
we implemented the collaborative filtering system (henceforth LLR system) which is similar to that of Harada& Masuda (2010) and compared its performance withAmazon. It was found that LLR system could not outperform Amazon. Library loan records contain users' privacy and collaborative filtering usually requires muchcomputer resources. We should pay attention to whether
LLR system is really effective enough to worth its cost.
Keywords Library Loan Records, Book Recommendation, Collaborative Filtering, Amazon, Privacy
DOI:I. INTRODUCTION
Library loan records are attracting people's attentions
who are trying to produce new library services. Amongthem is the book recommendation system using collaborative filtering based on loan records (henceforth
LLR system). However, library loan records containusers' privacy and collaborative filtering usually requires much computer resources. Is it really effective
enough to worth its cost? Against this background, weimplemented LLR system which was similar to that ofHarada & Masuda (2010) and compared its performancewith Amazon. If the latter outperformed the former, wemight be able to say that Amazon is an inexpensive alternative for book recommendation system. The loan
records we used for our experiment were those of one
Japanese university library.
II. METHODS
A. Recommendation Algorithm
In this paper, we call the user to whom we would like torecommend books as "active user". Similarly, we callusers whose library loan records are used for recommendation as "sample users".
In brief, our system calculates the similarity of library loan records between active user and sample users
and recommends books that several sample users whoare similar to active user have borrowed (and active user
has not borrowed). For instance, if a sample user exists
who borrowed books {a, b, c, d, e} and the active user
has borrowed books {a, b, c, d}, our system recommends book e to the active user. The detail is as follows:(a) Our system calculates the similarity score pai be-

tween active user a and sample user i as follows:p ai kYai( s ak  s ' a )( sik  s 'i )kY (sak  s 'a ) 2aikY (sik  s 'i ) 2

(1)

ai

where Yai represents a set of books that the active user aand sample user i evaluated in common (in fact, Yairepresents all the books the library holds as we mention
later). sij represents a score the user i gave to the book j.
s 'i represents the average score that the user i gave tothe books.
(b) Then our system recommends book j whose scoreŝ aj is high:sˆaj  iX pai ( sij  s 'i )(2)where X represents all the sample users. The score wedefined is slightly different from that of Kamishima(2008) and Resnick et al. (1994). In order to adopt score
definition by Harada & Masuda (2010), we regarded thebooks which were not borrowed were scored 0 by theuser. Therefore, all books the library holds were regarded to be scored by both active user and sample users (otherwise, we cannot calculate pai).
According to Harada & Masuda (2010), the scoresare defined as follows: (1) the score the sample user
gave to book is either 0 (he/she has not borrowed thatbook) or 1 (borrowed that book). (2) the score the active
user gave were determined based on the subject of thebook. The books which have the most popular Japanese
decimal classification number (henceforth NDC. It
ranges from 0 to 9) among the set of books the user borrowed are given score 1. The second, third and fourthmost popular books are given scores 0.5, 0.25, 0.125,..,
respectively. The loan records are divided by monthaccording to Harada & Masuda (2010). Therefore, forinstance, sample user X's loan record in April 2009 andin May 2009 are regarded as different loan records ofdifferent sample users.
B. Library Loan Records
The library loan records of one Japanese university library were used for our experiment. Each record consists of user ID, book ID, the dates the book was borrowed/returned, NDC of the book, etc. These recordswere collected from 2006 to 2010. The number of themis approximately two million.
C. Evaluation
Four students were asked to be subjects of our experiment. Two of them are Ph.D. course students (henceforth students A and B) and the others are undergraduates of fourth grade (students C and D). They all belong to the library and information science course. Student A and B borrowed 58 and 207 books (tokens) during the previously mentioned period. Students C and D
borrowed 82 and 11 books, respectively.
The subjects were asked to indicate one book R
which they were interested in and the library holds.
Then:(1) We put it into LLR system. The system recommends
six books based on R and loan records of each subject.
(2) We put R into Amazon.co.jp and obtain six books
that the Amazon recommends as "The customer whobought R also bought these six books". Then thebooks that the library holds among these six werechosen for the final recommendation.
These two sets of recommended books were mixed andshown to the subjects. They were asked to rate them 3, 2,
1, 0 and x as follows:
3: I already have that book (the best recommendation).
2: I am strongly interested in that book (second best).
1: I am interested in that book.
0: I am not interested in that book.
x: I have no idea.
III. RESULTS & DISCUSSIONS
The rates given to each recommended book by subjects
A, B, C and D are shown in Tabs. 1, 2, 3 and 4, respectively. We can see in Tab. 1, for instance, that graduate
student A gave "1" to four books (i.e., "Libraries in Finland & Sweden", "Introduction to Cultural Informatics",...) which were recommended by LLR system andits ratio is 67% (=4/6). Likewise, she gave "3" to threebooks (i.e., "Orientarism Vol.1", "Orientarism
Vol.2", ...) which were recommended by Amazon andits ratio is 60% (=3/5). By comparing the rates given tobooks which were recommended by LLR and Amazon,
we can see that Amazon obtained relatively betterscores than LLR and thus showed better performance.
For instance, while undergraduate student D showed nointerest in books recommended by LLR, she gave rates"2" and "1" to one and two books recommended byAmazon, respectively (in Tab. 4).
As we previously mentioned, undergraduate student
D borrowed only 11 books from library (although she isin the fourth grade, i.e., she has been in university threeyears and a half). To such students, LLR system cannot
recommend adequate books. Unfortunately, we thinkshe is not an exceptional case in Japan. While the dif-ference in recommendation performance between LLRand Amazon to student B (who borrowed 207 books) issmall, those to students A and B are not negligible.
The loan records in university libraries have inherent weakness compared to Amazon purchase records.
Borrowing books in libraries is free of charge and someusers carelessly borrow books which have no relation toeach other and to their needs. That brings noise to recommendation system. Besides, students borrow various
books together which are unrelated especially in the endof the semester to prepare term reports and term exams.
That might bring another noise to recommendation.
IV. CONCLUSIONSTo show the effectiveness or limitation of using libraryloan records for book recommendation, we implemented the collaborative filtering system which is similar to that of Harada & Masuda (2010) and compared its
performance with Amazon. As we were concerned
about, the LLR system could not outperform Amazon.
We do not claim that libraries should not adopt newtechnology but library loan records contain users' privacy and collaborative filtering usually requires muchcomputer resources. We should pay attention to whether
LLR system is really effective enough to worth its cost.
REFERENCES
Harada, T., “The book recommendation system using
library loan records,” Digital Libraries, 36, 22-31(2009). (Text in Japanese)
Harada, T. and K. Masuda, “A trial approach of weighting for library loan records for developing a book
recommendation system,” Digital Libraries, 38,
54-66 (2010). (Text in Japanese)
Hwang, S. Y. and S. M. Chuang, “Combining article
content and Web usage for literature recommendation in digital libraries,” Online Information Review,28(4),260-272(2004).
http://www.emeraldinsight.com/journals.htm?articl
eid=862259&show=abstract
Kamishima, T., “Algorithms for Recommender Systems(2),” Journal of Japanese Society for Artificial Intelligence, 23(1),89-103. (2008) (Text in Japanese)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. andRiedl, J., “GroupLens: An Open Architecture forCollaborative Filtering of Netnews,” Proceedingsof the Conference on Computer Supported Cooperative Work, 175–186 (1994).
Saura, T. and K. Tsuji, “Attitude Survey on Utilizing
the Public Library Use Records,” Proceedings ofthe 57th Japan Society of Library and Information
Science,1-4(2009).
http://slis.sakura.ne.jp/jslis57_saura.pdf (Text inJapanese)
Whitney, C. and L. Schiff, “The Melvyl Recommender
Project: Developing library recommendation services,” D-Lib Magazine, 12(12), (2006).
http://www.dlib.org/dlib/december06/whitney/12w
hitney.html

Books Recommended by Library Loan RecordRate
310(%) Title and Other Bibliographic Data
17 Incidents related to Intellectual Freedom in Libraries(「図書館の自由に関する事例集」日本図書館協会，2008)
67 Libraries in Finland & Sweden(図書館計画施設研究所，図書館流通センター，1994)
Introduction to Cultural Informatics(「文化情報学入門」村上征勝ら，勉誠出版，2006)
Libraries in Norway & Sweden(図書館計画施設研究所，リブリオ出版，1996)
Cultural History of Mimeograph(「ガリ版文化史...」田村紀雄ら，新宿書房，1985)
17 Design of Interactive Video(「インタラクティヴ...」Iuppaら，パーソナル..，1994)
Books Recommended by Amazon

Rate31(%) Title and Other Bibliographic Data
60 Orientarism Vol.1(「オリエンタリズム（上）」サイード，平凡社，1993)
Orientarism Vol.2(「オリエンタリズム（下）」サイード，平凡社，1993)
Media Control: Democracy and International Society...
(「メディア・コントロール...」チョムスキー，集英社，2003)
40 What is Palestine?
(「パレスチナとは何か」サイード，岩波書店，1995)
Said that Said Tells(「サイード自身が...」サイードら，紀伊國屋書店，2006)
Table 1. Books recommended to graduate student A

Books Recommended by Library Loan RecordRate
310x(%) Title and Other Bibliographic Data
17 Resource Organization Practice(「資料組織演習」岡田靖ら，樹村房，2007)
17 Utilizing Namazu Full-Text Search Engine(「Namazuシステムの...」馬場肇，ソフトバンク...，2003)
50 Skills for Information Retrieval(「情報検索のスキル...」三輪眞木子，中央公論新社，2003）
Cognitive Psychology for Sentence Understanding(「文章理解についての...」邑本俊亮，風間書房，1998)
Strategies of Discourse Comprehension(Teun A. van Dijk et al.，Academic Press，1983)
17 Symbolic System which Produces Meanings(「意味を生み出す記号...」加藤雅人，世界思想社，2005)
Books Recommended by AmazonRate
310x(%) Title and Other Bibliographic Data
20 Amusing Ruby(「たのしいRuby」高橋征義ら，ソフトバンク...，2010)
20 Meta Programming Ruby(「メタプログラミングRuby」Perrottaら，アスキー...，2010)40 Developing Web Application by Ruby on Rails 2(「「Ruby on Rails 2」...」北野光一ら，工学社，2009)
Agile Web Application Development by Rails(「Railsによるアジャイル...」Sam Rubyら，オーム社，2009)20 Ruby Handbook(「Ruby逆引きレシピ...」島田浩二ら，翔泳社，2009)
Table 2. Books recommended to graduate student B

Books Recommended by Library Loan RecordRate
310x(%) Title and Other Bibliographic Data
17 Classifying Books(「本を分類する」緑川信之，勁草書房，1996)
33 Understanding and Utilizing Pathfinder，LCSH and Metadata(「パスファインダー...」鹿島みづきら，紀伊國屋書店，2005)
Introduction to Book Classification(「図書分類法要説 改訂版」加藤宗厚，理想社，1950)
17 Approach to Subject Information(「主題情報へのアプローチ」丸山昭二郎ら，雄山閣出版，1990)
33 Song of Ariran: A Korean Communist in...
(Nym Wales et al.，Ramparts Press，1941)
Metadata and its applications in the digital library...
(Jia Liu，Libraries Unlimited，2007)
Books Recommended by AmazonRate21(%) Title and Other Bibliographic Data
50 Survey Tips of Librarians(「図書館のプロが伝える調査...」高田高史ら，柏書房，2009)
Using Libraries 100%: Tips to Find Resources(「図書館を使い倒す...」千野信浩，新潮社，2005)
50 How to Get Ideas in Libraries(「図書館が教えてくれた発想法」高田高史，柏書房，2007)
Ask Librarians!
(「図書館に訊け!」井上真琴，筑摩書房，2004)
Table 3. Books recommended to undergraduate student C

Books Recommended by Library Loan Record
Rate (%) Title and Other Bibliographic Data
0 100 School Management and School Library(「学校経営と学校図書館」古賀節子ら，樹村房，2002)
0How to Read Science Books(「科学の本の読み方すすめ方」板倉聖宣ら，仮説社，1993)
0
Forty Years of School Librarian Pursueing Dream(「夢を追い続けた学校司書...」五十嵐絹子，国土社，2006)
0Introduction to Linear Algebra(「線型代数入門」斎藤正彦，東京大学出版会，1966)
0What the School Library should be(「学校図書館経営の在り方」尾原淳夫，駸々堂，1950)
0Introduction to Curriculm Study(「カリキュラム研究入門 新版」安彦忠彦，勁草書房，1999)
Books Recommended by AmazonRate
210(%) Title and Other Bibliographic Data
25 Perl Quick Reference(「Perlクイックリファレンス」Ellen Sieverら，オーム社，2000)
50 CGI Programming(「CGIプログラミング」Scott Guelichら，オーム社，2001)
Perl Cook Book(「Perlクックブック」Tom Christiansenら，オーム社，2004)
25 makemake 改訂版」Andrew Oramら，オーム社，1997)
Table 4. Books recommended to undergraduate student D