Use of Library Loan Records for Book Recommendation
Keita Tsuji1, Erika Kuroo2, Sho Sato3, Ui Ikeuchi4, Atsushi Ikeuchi5, Fuyuki Yoshikane6 and Hiroshi Itsumura7
1,5,6,7

Faculty of Library, Information and Media Science
Graduate School of Library, Information and Media Studies 3,4
College of Knowledge and Library Sciences, School of Informatics 2
University of Tsukuba
1-2 Kasuga, Tsukuba-city, Ibaraki-ken 305-8550, Japan{keita1, min2fly3, oui4, atsushi5, fuyuki6, hits7}@slis.tsukuba.ac.jps0811586@u.tsukuba.ac.jp2

Abstract—To determine the most effective book
recommendation method for libraries, we conducted arecommendation experiment using (1) collaborative filtering
based on the library loan records, (2) association rule mining
based on the same data, and (3) Amazon. The library loan
records of a certain university library for the period 2006 to2011 were used. We recommended books to 33 students andasked them to describe the books' level of interest. The
results show that books recommended by collaborative
filtering were least favorably evaluated, followed by thoserecommended by association rule and Amazon.
Collaborative filtering carries the risk of breaching users'
privacy and its computational costs are higher compared tothose of association rule mining. Therefore, if werecommend books based on library loan records, association
rule mining should be adopted instead of collaborative
filtering. In addition, given the fact that therecommendations by Amazon were most favorably
evaluated, utilization of Amazon should also be considered.
Keywords—book recommendation; library loan records;
university libraries; collaborative filtering; association ruleI.

INTRODUCTION

The effective use of library loan records for generating
recommendations has been actively discussed amonglibrarians and LIS researchers. One method is torecommend books to users based on the loan records,
which some libraries are actually doing. Some studies haveproposed approaches for implementing this method.
However, two issues have not been clarified: (1) Whichmethod yields the most effective book recommendation
system based on the library loan records, and (2) whether arecommendation based on loan records is more effective
than that based on other information.
Concerning issue (1), to date, various recommendation
methods have been proposed based on usage records,
among which collaborative filtering and association rulemining are the most popular. However, it has not beenclarified which method is the most effective for book
recommendation in libraries. For instance, [1] usedcollaborative filtering based on library loan records but did
not compare this with other methods, including association
rule mining. Each method has different characteristics. For

instance, there is a risk of the user's privacy being
breached in the case of collaborative filtering since theloan records of each user have to be retained. 12 On theother hand, association rule mining methods do not have toretain each user's records and the risk of a breach ofprivacy being incurred is small. In addition, thecomputational cost, that is, the time required to determine
which book should be recommended, is generally lower inthe case of association rule mining than of collaborative
filtering. Based on the above, if the association rule mining
has a greater recommendation ability than collaborative
filtering, libraries should recommend books using thismethod, which is effective, safe, and quick.
Amazon's book recommendation system is popular.
Amazon provides a widespread service and its application
programming interface (API) is available. If we submit
information about a book that a user found interesting toAmazon’s API, it returns information about other books as"Customers who bought this book also bought thesebooks." By extracting the books that the university libraryholds from these recommendations and recommending
them to the library user, we can easily realize a book
recommendation system at a library. Concerning issue (2),it can therefore be said that, if such a recommendation isevaluated by users more favorably than a recommendation
based on library loan records, libraries can effectively
recommend books without using the loan records, as longas Amazon does not go out of business, stop providing theinformation, or change the recommendation method
significantly.
Based on this background, we asked 33 students of acertain university (henceforth “T University”) to name one
book in which they were interested, recommended books
to them based on this choice and on their library loan
records, and asked them to indicate to what extent they
were interested in the recommended books. We used threerecommendation methods: (a) collaborative filtering as
1

We assume that there is no perfectly secure system and if we retainsome data, they can be stolen or extracted from the system.
2
However, we should note that retaining each user’s loan records could
be beneficial for recommending books, although at present we do notknow how to achieve this.

proposed by [1], (b) association rule mining, and (c)Amazon.
In the present paper, the library loan records to which
we refer consist of the ID of the user, the ID of thematerial the user borrowed, bibliographic data, and thecheckout date. A book recommendation consists ofrecommending that a user borrow a book by showing him
or her the bibliographic data: title, author, publisher, andpublication year of the book.
II.

RELATED STUDIES

Among the methods proposed in the literature on
collaborative filtering, the one proposed by [2] is regarded
as a representative method of the field. This method firstfinds similar Netnews readers based on the evaluation
scores they gave for a news item. It then recommends to
the reader the news items that he/she has not read and towhich the similar readers gave a high evaluation score.
Since the study of [2], collaborative filtering has beenused in various fields. However, there have been few
studies on using collaborative filtering to recommend
books at libraries. We can name only [1], [3], and [4]. Thismay be because usage of loan records used to be almost
taboo in the library world.
Reference [3] used collaborative filtering software
called Vogoo PHP Pro v2.2 over 960,078 loan records for
8,808 users of a university library. Reference [1] also used
this software. The significant difference between [1] and[3] is that the former used NDC (Nippon DecimalClassification) for weighting books, and the loan records
were divided according to the checkout month/year.
Reference [1] tested five patterns with regard to suchweighting and identified the most effective pattern.
Reference [4] compared collaborative filtering basedon library loan records with Amazon's recommendation
system. However, only four subjects participated in theexperiment, and a comparison with the association rulemining method was not conducted.
In addition to the above, [5] proposed arecommendation method that used a weighted graphmodel that is similar to the association rule method.
References [6], [7], and [8] proposed somerecommendation methods, but did not conduct
experiments to evaluate their effectiveness.
III.

DATA

A. Library Loan Records
We obtained 1,990,797 loan records from the T
University library (checkout dates ranged from January 2,
2006 to March 31, 2011). Of these records, 864,704 werefor books checked out by undergraduate students, 989,641
by graduate students and faculty members, and 136,452 byothers. We used 1,854,345 loan records of undergraduate
students, graduate students, and teachers for this study.
The number of types of books borrowed was 435,817, andthe number of users was 39,442. The number of so-calledbaskets (set of books that were borrowed together) was
708,951.

B. Subjects
Thirty-three students majoring in library andinformation science at T University participated as
subjects in our experiment. They comprised 7 graduate
students, 17 fourth-year undergraduate students, and 9second-year undergraduate students. For convenience's
sake, we will call these three groups “groups whose grades
are different,” although graduate students andundergraduate students are not regarded as being ofdifferent “grades” according to the normal definition.
C. One Book that the Subject Would Like to Borrow at
Present
Subjects were asked to give a title (and otherbibliographic information if necessary) of one “book that I
would like to borrow from T University library at present,
for research or study purposes.” This information wasused to generate recommendations based on association
rule mining, as well as the Amazon recommendation
system.
IV.

RECOMMENDATION METHOD

A. Collaborative Filtering
References [1] and [3] used software called Vogoo
PHP Pro v2.2, as previously mentioned. However, themethod used by this program to calculate and recommend
books is not given. In addition, this software is difficult toobtain since the distribution company no longer exists.
Therefore, we adopted the method of [2] and a weighting
method that [1] used collaborative filtering.
First, we will explain the method of [2]. Henceforth,
we will call the target user to whom the systemrecommends books the “active user” and the other user the“sample user.” The method of [2] recommends to theactive user the items for which sample users who aresimilar to the active user gave high evaluation scores (and,of course, the items that the active user has not evaluated).
First, we define the notations as:: The set of items that active user and sample user
evaluated in common.
, : The evaluation scores that active user andsample user gave to item .
̅ , ̅ : The average of evaluation scores that active user，and sample user gave to the items.
: The set of users who evaluated item .
The similarity,, between active user and sample user
is defined as the correlation coefficient=∑𝑘∈𝑌𝑎𝑖(√∑𝑘∈𝑌𝑎𝑖(

𝑘−

𝑘−

̅ )(

𝑘−

̅ )2 √∑𝑘∈𝑌 (
𝑎𝑖

̅)
𝑘−

̅ )2

The method of [2] recommends to the active user theitems whose scores, ̂ , are high. They are defined as

̂

= ̅ +

∑ ∈𝑋𝑗

(

∑ ∈𝑋𝑗|− ̅𝑖 )|

We will now explain the weighting method of [1]. We
first divide the loan records of one sample user into setsaccording to the checkout month/year and consider thesesets as the loan records of different sample users.
Evaluation scores are given to each book in the active
users' loan records based on its NDC category (top level,i.e., from 0 to 9). An evaluation score of 0.5(n-1) is given to
the book whose NDC category is the n-th most frequently
occurring among the books that the active user borrowed.
For instance, an evaluation score of 0.25 is assigned to thebook whose NDC category is the third most among thebooks the active user borrowed.
B. Association Rule Mining
When a user borrowed books, ( =) , at} a “transaction.”
one time, we will call the set {For instance, when a user borrowed three books, , , and, at one time, the transaction can be represented as{}. From this transaction, we can extract a rule “theuser who borrows bookalso borrows book .” In
addition, we can also extract a rule, “the user who borrows
books and at one time also borrows book .” Basedon all the transactions of all the users, the association rulemining extracts the frequently observed, and, in that sense,“reliable” rules.
C. Amazon
We input to the Amazon recommendation system thebibliographic data of the books mentioned in Section IIIC
and obtained manually the bibliographic data of the books
recommended by Amazon as “the customer whopurchased this book also purchased these books.” Fromthese books, we extracted the books that the T University
library holds and recommended them to the subjects.
V.

EVALUATION METHOD

The bibliographic data of the books recommended bycollaborative filtering, association rule mining, and the
Amazon system were shown to the subjects (thebibliographic data consists of title, author, publisher, andpublication year). The subjects were then asked to describe
their level of interest in each book using the following
five-point scale, which is similar to that used by [1].
2: Very interested
1: Interested
0: Not interested
x: Have no idea
A: Already bought or have read before

VI.

RESULTS

A. Overall Results
The results are shown in Table I. In Fig. 1, “x” and “0”are combined in a bar chart presentation of the results.
As seen in Table I and Fig. 1, the proportion ofrecommended books in which the subjects were “2: Veryinterested” was the largest in the case of Amazon’s
system, followed by association rule mining andcollaborative filtering (28.7%, 18.6% and 11.1%,
respectively).
Likewise,theproportionofrecommendation results that received a rating of “1:Interested” was the largest in the case of Amazon,
followed by association rule mining and collaborative
filtering (40.2%, 37.1% and 26.3%, respectively). If weregard “A: Already bought or have read before,” “2: Veryinterested,” and “1: Interested” as a “positive evaluation,”the proportion of this evaluation category was the largest
in the case of Amazon, followed by association ruleminingandcollaborative
filtering(79.6%(=10.7+28.7+40.2), 60.0% and 43.0%, respectively).
Significant differences between these proportions wereobserved at the level of 0.01. Based on the above, we cansay that the recommendation performance of association
rule mining is better than that of collaborative filtering,and that that of Amazon is even better.
B. Results According to Grade
The evaluation results for the graduate students, fourthyear
undergraduate
students
andsecond-year
undergraduate students are shown in Tables II to IV and
Figs. 2 to 4. From these results, we can see that for all
grades the proportion of positive evaluations is the largest
in the case of Amazon, followed by association rulemining and collaborative filtering.
C. Results According to the Number of Books Borrowed
We divided the subjects into two groups: A, subjects
who borrowed 21 books or more, and B, those whoborrowed fewer than 21 books. Group A and B consistedof 20 and 13 subjects, respectively. The evaluation results
for these two groups are shown in Tables V and VI and
Figs. 5 and 6. In Group A, the proportion of collaborative
filtering recommendations that received positive
evaluations is 49.2% (6.7+9.2+33.3). On the other hand,
this figure is just 33.3% in Group B. A significant
difference between these proportions was observed at thecollaborative filtering score level of 0.05, which indicatesthat the performance of collaborative filtering drops in thecase of those who borrowed fewer books.
Reference [1] limited the selection of subjects tostudents who borrowed 21 books or more. However, thestudents who borrowed fewer than 21 books account for
approximately 40% of our subjects. If our subjects are a
representative sample of Japanese university students, therecommendation results of collaborative filtering forJapanese students may be worse than that indicated by [1].

D. Results According to Collaborative Filtering Score
For the books to which the collaborative filtering
assigned a score of 0.1 or higher, the proportion of “2:Very interested” was 25.0% (Table VII and Fig. 7).
However, in this case as well, the proportion of positive
evaluations still falls short of that for the association rulemining.
E. Results by NDC Categories
We divided the subjects into groups according to the
NDC categories of the books they would like to borrow at
the present and examined the recommendation
performances for each group. The results show that the
proportions of positive evaluation were not different
among these groups. We also divided the subjects into
groups according to the NDC categories most frequently
observed in the books they borrowed; however, the results
were the same. Therefore, we may reasonably say that therecommendation performance does not significantly differ
according to NDC category.

association rule mining and by Amazon's system. Futuretasks include (a) generating recommendations bycollaborative filtering using various other parameters, (b)incorporating content-based filtering in our system, (c)incorporating seasonal change of the students' level ofinterest in our system, and (d) utilization of loan records atother university libraries. If university libraries in Japan
cooperate with each other and allow others to use the loan
records upon protecting users' privacy, a sufficient volume
of information can be used for recommendation.
REFERENCES
[1]

[2]

[3]

VII. DISCUSSIONAs mentioned previously, the positive evaluation by
subjects of the generated recommendations was highest for
Amazon’s system, followed by association rule mining
and collaborative filtering. 3 This was true when subjects
were divided according to their grade and according to the
NDC category of the books they borrowed. As discussed
earlier, collaborative filtering has some drawbacks, ascompared with association rule mining. Therefore, if werecommend books on the basis of library loan records,
association rule mining should be adopted instead ofcollaborative filtering. In addition, given the facts that (1)the recommendations of Amazon's system were mostfavorably evaluated, and (2) Amazon's system does notneed any library loan records and its use carries no risk of
breaching the users' privacy, Amazon’s recommendation
system should also be considered for book
recommendation. However, it should be noted that
Amazon's recommendation system is like a black box, andthe company might stop providing the API.
VIII. CONCLUSIONSTo determine the most effective method for generatingbook recommendations in libraries, we conducted arecommendation experiment using (1) collaborative
filtering based on the library loan records, (2) association
rule mining based on the same data, and (3) the Amazon
recommendation system. The results show that books
recommended by collaborative filtering were least
favorably evaluated, followed by those recommended by3

It is true that these methods receive different types of inputs and thus
they were not compared under the same condition. Unlike collaborative
filtering, association rule mining and Amazon's system do not use thehistory of a user’s interest, and a book that each user is interested in
now has to be used as input. However, it should be noted that thesedifferences are intrinsic to the three methods and cannot be modified orcoordinated.

[4]

[5]

[6]

[7]

[8]

T. Harada and K. Masuda, “A trial approach of weighting forlibrary loan records for developing a book recommendation
system,” Digital Libraries, no. 38, 2010, pp. 54-66 (text inJapanese).
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl,“GroupLens: an open architecture for collaborative filtering of
netnews,” Proceedings of ACM 1994 Conference on Computer
Supported Cooperative Work, 1994, pp.175-186.
T. Harada, “The book recommendation system using library loan
records,” Digital Libraries, no. 36, 2009, pp. 22-31 (text inJapanese).
K. Tsuji et al., “Use of library loan records for book
recommendation,” Proceedings of the International Conference on
Integrated Information (IC-ININFO 2011), 2011. (No Pagination).
C. Whitney and L. Schiff, “Melvil Recommender Project:
developing library recommendation services,” D-Lib Magazine,
vol.
12,
no.
2,
2006.
<http://www.dlib.org/dlib/december06/whitney/12whitney.html>.
[Accessed: May 5, 2012]
S. Shirgaonkar, T. Rajkumar and V. Singh, “Application of
improved apriori in university library,” International Conference
and Workshop on Emerging Trends in Technology (ICWET 2010),
2010, pp. 535-540.
C. Chen and A. Chen, “Using data mining technology to provide a
recommendation service in the digital library,” The Electronic
Library, vol. 25, no. 6, 2007, pp. 711-724.
Y. Luo, J. Le and H. Chen, “A privacy-preserving book
recommendation model based on multi-agent,” Proceedings of the2009 Second International Workshop on Computer Science andEngineering, 2009, pp. 323-327.

Table III. Evaluations of fourth-year undergraduate
students

Table I. Overall evaluation resultsA
Amazon13 ( 10.7 )Association
6 ( 4.3 )Collaborative 11 ( 5.6 )

235 ( 28.7 )
26 ( 18.6 )
22 ( 11.1 )

1049 ( 40.2 ) 21 ( 17.2 )
52 ( 37.1 ) 52 ( 37.1 )
52 ( 26.3 ) 94 ( 47.5 )

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

AmazonAssociation
Collaborative

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

x
Total
4 ( 3.3 ) 122
4 ( 2.9 ) 140
19 ( 9.6 ) 198

0, x: Not
interested
1: Interested
2: Veryinterested
A: Alreadybought or …

AmazonAssociation
CollaborativeA
7 ( 11.7 )
2 ( 3.0 )3 ( 3.1 )

2
13 ( 21.7 )7 ( 10.6 )9 ( 9.4 )

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

1
0, x
Total
26 ( 43.3 ) 14 ( 23.3 )
60
28 ( 42.4 ) 29 ( 43.9 )
66
18 ( 18.8 ) 66 ( 68.8 )960, x: Not
interested
1: Interested
2: Veryinterested
A: Alreadybought or …

Figure 1. Overall evaluation results

Figure 3. Evaluations of fourth-year undergraduate
students

Table II. Graduate students

Table IV. Second-year undergraduate studentsA
5 ( 17.2 )4 ( 11.1 )
6 ( 12.5 )

2
9 ( 31.0 )5 ( 13.9 )4 ( 8.3 )

1
0, x
Total
10 ( 34.5 )
5 ( 17.2 )
29
14 ( 38.9 ) 13 ( 36.1 )
36
17 ( 35.4 ) 21 ( 43.8 )480, x: Not
interested
1: Interested
2: Veryinterested
A: Alreadybought or …

Figure 2. Evaluations of graduate students

A
AmazonAssociation
Collaborative

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

1(
0(
2(

3.0 )
0.0 )
3.7 )

2
13 ( 39.4 )
14 ( 36.8 )9 ( 16.7 )

1
0, x
Total
13 ( 39.4 )
6 ( 18.2 )
33
10 ( 26.3 ) 14 ( 36.8 )
38
17 ( 31.5 ) 26 ( 48.1 )540, x: Not
interested
1: Interested
2: Veryinterested
A: Alreadybought or …

Figure 4. Evaluations of second-year undergraduate
students

Table V. Evaluations of subjects who borrowed 21 books
or moreA
Amazon11 ( 14.5 )Association
5 ( 6.1 )Collaborative 8 ( 6.7 )

218 ( 23.7 )
13 ( 15.9 )
11 ( 9.2 )

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

1
0, x
Total
29 ( 38.2 ) 18 ( 23.7 )
76
30 ( 36.6 ) 34 ( 41.5 )
82
40 ( 33.3 ) 61 ( 50.8 ) 120

0, x: Not
interested
1: Interested
2: Veryinterested
A: Alreadybought or …

Figure 5. Evaluations of subjects who borrowed 21 books
or moreTable VI. Subjects who borrowed fewer than 21 books
A
AmazonAssociation
Collaborative

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

2(
1(
3(

4.3 )
1.7 )
3.8 )

2
17 ( 37.0 )
13 ( 22.4 )
11 ( 14.1 )

1
0, x
Total
20 ( 43.5 )7 ( 15.2 )
46
22 ( 37.9 ) 22 ( 37.9 )
58
12 ( 15.4 ) 52 ( 66.7 )78

0, x: Not
interested
1: Interested
2: Veryinterested
A: Alreadybought or …

Figure 6. Evaluations of subjects who borrowed fewer
than 21 books

Table VII. Results according to collaborative filtering
score
A
Score≧0.1
2(
Score≧0.05
3(
Total
11 (

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

6.3 )
3.3 )
5.6 )

2
8 ( 25.0 )
13 ( 14.4 )
22 ( 11.1 )

1
0, x
Total
8 ( 25.0 ) 14 ( 43.8 )
32
20 ( 22.2 ) 54 ( 60.0 )
9052 ( 26.3 ) 113 ( 57.1 ) 198

0, x: Not
interested
1: Interested
2: Veryinterested
A: Alreadybought or …

Figure 7. Results according to collaborative filtering score