Available online at www.sciencedirect.com

Procedia - Social and Behavioral Sciences 00 (2013) 000–000
www.elsevier.com/locate/procedia

3rd International Conference on Integrated Information (IC-ININFO)Book Recommendation based on Library Loan Records andBibliographic Information
Keita Tsujia*, Nobuya Takizawab, Sho Satoc, Ui Ikeuchid, Atsushi Ikeuchia,

Fuyuki Yoshikanea and Hiroshi ItsumuraaaFaculty of Library, Information and Media Science, University of Tsukuba, 1-2 Kasuga, Tsukuba-city, Ibaraki 305-8550, Japan
College of Knowledge and Library Sciences, School of Informatics, University of Tsukuba, 1-2 Kasuga, Tsukuba-city, Ibaraki 305-8550, Japanc
Faculty of Social Studies, Doshisha University, Karasuma Higashi-iru, Imadegawa-dori, Kamigyo-ku, Kyoto 602-8580, Japand
Graduate School of Library, Information and Media Studies,University of Tsukuba, 1-2 Kasuga, Tsukuba-city, Ibaraki 305-8550, Japanb

Abstract
In order to show the effectiveness of using (a) library loan records and (b) information about book contents as a basis for book
recommendations, we entered various data into a support vector machine (SVM), used it to recommend books to subjects, andasked them for evaluations of the recommendations that were given. The data that we used were (1) confidence and support
with an association rule that was based on the loan records, (2) similarities between book titles, (3) matches/mismatches
between the Nippon Decimal Classification (NDC) categories of the books, and (4) similarities between the outlines of thebooks in the BOOK Database. The subjects were 32 students who belonged to T University. The books that we recommended
and the loan records that we used were obtained from the T University Library. The results showed that the combinations of (1),(2), (3) and (1), (2) were rated more favorably by the subjects than the other combinations. However, the books that wererecommended by Amazon were rated even more favorably by the subjects. This is a topic for further research.
© 2013 The Authors. Published by Elsevier Ltd.
Selection and peer-review under responsibility of the 3rd International Conference on Integrated Information.
Keywords: Book Recommendation; Recommender System; Library Loan Records; Support Vector Machine; SVM* Corresponding author. Tel.: +81-29-859-1428; fax: +81-29-859-1162.
E-mail address: keita@slis.tsukuba.ac.jp

1877-0428 © 2013 The Authors. Published by Elsevier Ltd.
Selection and peer-review under responsibility of the 3rd International Conference on Integrated Information.

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1. Introduction
The effective use of library loan records for generating recommendations has been actively discussed amonglibrarians and library and information science researchers. One method is to recommend books to users based onloan records. Some studies have proposed approaches for implementing this method. However, most of thesemethods use only the information from loan records. Book titles and the Nippon Decimal Classification (NDC)
categories that have been assigned to the books have not been used. We contend that book titles, NDC categories,
and the outlines of books from the BOOK Database are important additional clues that can be used for the purposeof formulating effective book recommendations and that the optimum combination and weighting of these
additional clues can be determined through machine learning. In particular, the similarities between titles, matcheson the NDC category, similarities between the outlines in the BOOK Database, and association rules that are basedon the loan records can be used as “features” of learning data. Support vector machines (SVM) can use this data to
determine the weights of the features, perform automatic classifications, and generate recommendations.
In our experiment, subjects were asked to provide the name of “one book that currently interests the subject”
and a recommendation was made by the SVM based on the following information: (1) the confidence and support
from an association rule, (2) similarities between titles, (3) matches/mismatches between NDC categories, and (4)similarities between the outlines in the BOOK Database. Tsuji et al. (2011) (2012) found that Amazon’s book
recommendation system had better results than a recommendation system that was based only on an association
rule that used library loan records. In our experiment, book recommendations were conducted and comparisonswith Amazon were included.
In this study, the books that we recommended were books from the T University Library and the library loan
records were also from the T University Library. We recommended books to test subjects non-graphically (i.e., byshowing only the bibliographic data, such as the title, author, publisher, and publication year of the book).
2. Related Studies
There have only been a few studies of book recommendations based on library loan records. Harada (2009) andHarada & Masuda (2010) used collaborative filtering. Tsuji et al. (2011) (2012) used 1,854,345 loan records from39,442 users of the T University Library and recommended books to 33 undergraduate and graduate students basedon (1) the collaborative filtering method that was proposed by Harada & Masuda (2010), (2) an association rule,and (3) Amazon. They found that the evaluations of these methods were ranked from best to worst as Amazon,
association rules, and then collaborative filtering.
Whitney & Schiff (2006) proposed a recommendation method that used a weighted graph model that is similar
to the association rule method. Chen & Chen (2007), Luo et al. (2009), and Shirgaonkar et al. (2010) proposed
various recommendation methods, but did not conduct experiments in order to evaluate their effectiveness.
3. Data
3.1. Library Loan Records and Bibliographic Data of T University Library
We obtained 2,324,418 loan records from the T University library. The checkout dates ranged from January 2,
2006 to March 31, 2012. Of these records, 999,630 were for books that were checked out by undergraduate
students. 1,294,012 books were checked out by graduate students and faculty members. 30,776 books werechecked by other types of patrons. We used 2,293,642 loan records from undergraduate students, graduate students,and faculty members for this study. The number of types of books that were borrowed was 477,668 and thenumber of users was 44,571. The number of baskets (i.e., sets of books that were borrowed together) was 821,771.
For recommendation, we chose books to which the NDC categories were assigned in the T University Library.
The number of these books was 643,676. NDC categories have not been assigned to many of the books in the T
University Library collection that are written in English or other non-Japanese languages. As a result, the number
643,676 is much smaller than the total number of books that the T University Library holds.

Author name / Procedia - Social and Behavioral Sciences 00 (2013) 000–000

33.2. BOOK Database
The BOOK Database is a database that contains outlines and bibliographic information for books that have been
published recently in Japan. We used information for books that were published between 2005 and 2011. The
numbers of books with outlines were 44,143 (2005 editions), 45,327, 48,241, 48,214, 48,459, 45,654, and 44,652(2011 editions). Outlines have been assigned to approximately 75% of the books.
3.3. Subjects
Thirty-two students that were majoring in library and information science at T University participated as
subjects in our experiment. They included 13 graduate students, 12 fourth-year undergraduate students, and 7
second-year undergraduate students. For convenience’s sake, we will call these three groups “groups whose grades
are different,” although graduate students and undergraduate students are not regarded as being different “grades”
according to the normal definition.
3.4. One Book that the Subject Would Like to Borrow at Present
Subjects were asked to provide the title (and other bibliographic information, if necessary) of one “book that I
would like to borrow from T University library at present for research or study purposes” (henceforth, “a book thatcurrently interests the subject”). This information was used to generate recommendations based on an association
rule (confidence and support), similarities between the titles, similarities between the BOOK Database outlines,
matches between the NDC categories, and the Amazon recommendation system.
3.5. Learning Data
Two types of learning data were prepared for the SVM as follows.
(1) In 2011, Tsuji et al. (2011) asked 33 subjects to provide the name of one book that currently interested them.
Based on the books, they recommended 460 books using an association rule and they asked the subjects to evaluate
their degree of interest in the book that was recommended using the same criteria that are used in this study (to be
described later). In this way, they obtained 460 pairs of books. In this study, the pairs where the recommended
book was evaluated as “2: very interested” and “0: not interested” were considered as positive examples andnegative examples, respectively, and were used as the first set of learning data. The total number of pairs in thelearning set was 186 and the numbers of pairs of positive examples and negative examples were 59 and 127,
respectively.
(2) The second set of learning data was as follows. First of all, we divided the 32 subjects that we mentioned in
Section 3.3 into two groups, S and T. We presented the following sets of books to each member of group S and
asked for evaluations: (a) six books with the highest probability of belonging to positive examples (described later)
based on the output from an SVM that was using the learning data described above, (b) six books with the highest
similarities based on titles, (c) six books with the highest confidence levels based on the association rule, (d) six
books with the highest similarities based on the outlines in the BOOK Database, and (e) six books that wererecommended by Amazon. By the same way as (1), we obtained 172 pairs of learning data (i.e., positive examples
that the subjects evaluated as “2: very interested” and negative examples that they evaluated as “0: not interested”).
These results were combined with the first set of pairs to give a total 358 pairs that were used for SVM learning.
The numbers of pairs of positive and negative examples were 111 and 247, respectively.
The following section discusses the evaluations from the T group of book recommendations that were producedby the SVM. In addition, evaluation results obtained from 32 subjects are also discussed in regards torecommendation that does not require learning like (b), (c), (d), and (e) above. This is the reason that the number ofsamples is different depending on methods in the following Tables. Results based on the first pairs for learning arealso discussed at the end.

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4. Recommendation Method
4.1. Recommendations based on Similarities between the Titles and the Outlines in the BOOK Database
Books whose titles are similar to those of the “books that currently interest the subjects” may be favorably
evaluated by the subjects. In order to recommend such books, we calculated the similarities between titles (and theoutlines) as follows. (1) Titles (or outlines) of all the books held in T University Library as well as “books thatcurrently interest the subjects” are divided into words using the Japanese morphological analyzer MeCab ver.
0.994. (2) Single nouns and two noun sequences were extracted from the strings of words. (3) The vectors
consisting of the TFIDF (term frequency-inverse document frequency) from these single nouns and two noun
sequences were created for each book. The TFIDF of a single noun S concerning book A is defined as the“frequency of S in the title (or outline) of book A” multiplied by “log (the number of all the books held in T
University Library) / (the number of books whose titles (or outline) contain S in the title)”. (4) The similarity
between the titles (or outlines) of two books is defined as the cosine measure of their corresponding vectors. (5)The six books with the highest cosine measures (out of all the books held in T University Library) when comparedwith “books that currently interest the subject” were recommended to the subject. (6) Similarities between titles (oroutlines) in the above were also adopted as the feature (information used for classification) for recommendations
by the SVM, which we will mention later.
4.2. Recommendations based on an Association Rule
When a user borrows n books, Xi (i = 1, ..., n), at one time, we call the set {X1, …, Xn } a “transaction”. For
instance, when a user borrows three books, A, B, and C, at one time, this transaction can be represented as {A, B,
C}. From this transaction, we can extract a rule that “the user who borrows book A also borrows book B.” If werepresent this rule as A → B, where A is called the premise and B is called the conclusion, then we can also extract
other rules, such as A → C, B → A, B → C, C → A, and C → B. Based on all of the transactions from all of theusers, the association rule extracts the frequently observed, and, in this sense, useful and reliable rules.
The “confidence” and “support” are the most widely used measures for usefulness and reliability of the rule X→ Y, respectively. The level of confidence is defined as the ratio of “the number of transactions that contain X andY” against “the number of transactions that contain X”. The level of support is defined as the ratio of “the numberof transactions that contain X and Y” against “the number of all transactions”. We recommended six books to eachsubject based on the six rules with the highest confidence levels that have the “premise” X is “a book that currently
interests the subject.”
When recommending based only on an association rule, we did not consider the support for each rule. However,
the recommendations based on both confidence and support might be more effective. Therefore, we used both ofthem as features for the SVM that will be discussed in the next section.
4.3. Recommendation by SVM Using Multiple Information
It might be effective to recommend books based on multiple sources of information combined with optimal
parameters, rather than a single source of information (such as confidence by association rule). SVM is widelyused machine learning method that can use multiple parameters. We obtained evaluation results from subjects for
SVM learning (i.e., to determine optimal parameters for classifying books into those that should be recommended
and those that should not be recommended), which will be described in Section5. We used the following four
sources of information for SVM features:(a) Confidence and support from the association rule based on the library loan records(b) Similarities between book titles(c) Matches/mismatches on NDC category (if all of their classes, divisions, and sections matched, then 1; else 0)(d) Similarities between the outlines of the books in the BOOKDBHenceforth, we will refer to (a), (b), (c), and (d) as Loan Record, Title, NDC, and BOOKDB, respectively. We
examined the effectiveness of the following combinations of features:

Author name / Procedia - Social and Behavioral Sciences 00 (2013) 000–000

5(1) Title + Loan Record(2) NDC + Title(3) NDC + Title + Loan Record(4) NDC + Title + Loan Record +BOOKDBWe used LIBSVM ver. 3.12 and adopted the L1 soft margin SVM “C-Support Vector Classification” and the
RBF (Radial Basis Function) kernel. We used the easy.py script to obtain optimal parameters C (margin parameter
which determines the generalization ability) and γ (parameter which determines the influence a single training
example has). We also used the –b option to display the probability that a book belonged to the class of “books thatshould be recommended.” We selected six books whose probabilities were the highest based on the combinations(1)–(4) that are described above and recommended these books to the subjects. Therefore, a total of 24 books wererecommended to each of the subjects by the SVM.
4.4. Amazon
We entered the “books that currently interest the subjects” as input into the Amazon recommendation systemand obtained a list of the six books that were recommended by Amazon based on “the customer who purchased
this book also purchased these books.” From these books, we extracted the books that were in the collection at the
T University Library and recommended these books to the subjects.
5. Evaluation Method
The bibliographic data from the books that were recommended based on the above-mentioned methods wereshown to the subjects. The subjects were then asked to describe their level of interest in each book using thefollowing five-point scale that is similar to the scale that was used by Tsuji et al. (2011) (2012): “2: Veryinterested”, “1: Interested”, “0: Not interested”, “x: Have no idea”, and “A: Have already bought or read”.
6. Results
6.1. Overall Results
Table 1 displays the evaluation results for recommendations based on: (1) SVM with combination of various
features, (2) similarities of titles only, (3) confidence based on the association rule only, (4) similarities of theoutlines in the BOOKDB only, and (5) Amazon as described above. We can see in Table 1 that 96 books wererecommended using “NDC + Title + Loan Record” and that out of these books, 14 books were evaluated as “2:Very Interested” by the subject, thereby accounting for 14.6% (= 14 / 96 * 100) of the books that wererecommended. On the other hand, 192 books were recommended using similarities of titles only and out of thesebooks, 65 books were evaluated as “1: would like to read”, thereby accounting for 33.9% (= 65 / 192 * 100).
If “A: Already Bought or Read”, “2: Very Interested”, and “1: Interested” are considered to be “positive
evaluations” as they were in Tsuji et al. (2011) (2012), then according to Table 1, the method (not including
Amazon) with the highest proportion of positive evaluations was “NDC + Title + Loan Record” with a result of
71.9% (= 6.3 + 14.6 + 51.0). The second highest is “BOOKDB + NDC + Title + Loan Record” with a result of
65.7% (= 6.3 + 12.5 + 46.9). This was followed by “Title + Loan Record” with a result of 63.6% (= 3.1+ 24.0 +36.5). These values are all higher than the 53.1% (= 3.9 + 17.2 + 32.0) that was obtained for the case that only used
the loan records (i.e., only confidence with the association rule was used), the 54.2% (= 2.6 + 17.7 + 33.9) that was
obtained for the case using only the similarities of the titles, and the 62.1% (= 0.0 + 13.6 + 48.5) that was obtained
for the case using only the similarities of the outlines in the BOOK Database. Therefore, it has been shown thatrecommendations based on SVM using multiple sources of information lead to better results thanrecommendations that are based on methods that use a single source of information, such as titles or loan records.
On the other hand, recommendations from methods that did not use loan records (such as “NDC + Title”) were
evaluated less favorably than recommendations from methods that used loan records (such as “NDC + Title + Loan

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Record”). Therefore, book recommendations aided by the loan records were more effective than recommendations
based only on titles or NDC categories.
In regards to the proportion of books that were evaluated as “2: Very Interested” (not including Amazon), “Title+ Loan Record” had the highest proportion at 24.0%, followed by “Title” with 17.7%, and “Loan Record” with
17.2%. It should be noted that none of these include the NDC categories. This issue will be explained later.
Finally, none of the methods that were proposed in this study were as good as the Amazon method in regards toboth “2: Very Interested” and positive evaluations as described above. This issue will be researched in the future.
6.2. Results by Grade
The results were compiled by dividing the subjects into graduate students, fourth-year undergraduate students,and second-year undergraduate students as shown in Tables 2, 3, and 4. According to these Tables, for fourth-yearand second-year undergraduate students, the proportion of positive evaluations was the highest for “NDC + Title +Loan Record” at 75.0% (= 8.3 + 13.9 + 52.8) and 77.8% (= 0.0 + 27.8 + 50.0), respectively (excluding Amazon).
However, for graduate students, “NDC + Title + Loan Record” was in second place with a proportion of positive
evaluations of 66.6% (= 7.1 + 9.5 + 50.0) and “Title + Loan Record” was in first place with 73.8% (= 7.1 + 16.7 +50.0). When the proportions of positive evaluations in the section above are compared, it is in the order of graduate
students (66.6%) ＜ fourth-year undergraduate students (75.0%) ＜ second-year undergraduate students (77.8%).
Thus, recommendations that use NDC are better for undergraduate students. However, it may be better if NDC is
not used for recommendations to graduate students. This issue will be discussed later.
6.3. Results by Volume of Learning Data
As described in Section 3.5, two kinds of learning data were used in this study. One consisted of 186 pairs andthe other consisted of 358 pairs. Up to this point we have shown the results when the latter was used. Results fromthe learning data with 186 pairs are shown as in Table 5. We can see from Table 5 that percentage of positive
evaluations for “NDC + Title + Loan Record” was at 54.1% (= 2.6 + 18.2 + 33.3) when the learning data set issmall (186 pairs). The ratio is significantly smaller than that (71.9%) obtained when the learning data was large(358 pairs). Since the difference above was observed when the size of the learning data set was roughly doubled, it
is possible that the performance of the method in this study can be improved in the future by creating and using alarger learning data set.
7. Discussions
The results from this study indicated that the proportion of positive evaluations is the highest for “NDC + Title+ Loan Record” and that the proportion of “2: Very interested” is the highest for “Title + Loan record.” In fact,“NDC + Title + Loan Record” only recommended books that had the same NDC as the “one book that currently
interests the subject,” and “Title + Loan Record” recommended different books as well.
The shelves in a library can serve as locations that contain book recommendations. It is likely that users that areinterested in a concrete research theme and frequently borrow books at a library (i.e., users who have their favorite
shelves), have a certain understanding of the books that are related to their interest through their experiences at therelevant library shelves. Therefore, it is possible that these users were already aware of many of the books thathave the same NDC (i.e., book from their favorite shelves) as the one book that they are currently interested in.
The validity of this assumption seems to be supported by the facts that (1) the proportion of positive evaluations
for “NDC + Title + Loan Record” was in the order of second-year undergraduate students > fourth-year
undergraduate students > graduate students, and (2) for graduate students, the proportion of positive evaluations
was higher for “Title + Loan Record”, where books with different NDC categories were also recommended, than it
was for “NDC + Title + Loan Record”, where only books with the same NDC were recommended. Therefore, it
may be effective to switch between these two methods depending on the user. This could be accomplished bydetermining whether or not the student has favorite shelves, for instance, based on the year of the student or thenumber of books that the student has borrowed in the past.

Author name / Procedia - Social and Behavioral Sciences 00 (2013) 000–000

7Finally, we used a simple cosine measure for determining the similarities between titles. However, the value of
this measure tends to be higher when the titles are short. Many recent books have long titles, and older books tend
to have simpler and shorter titles. Therefore, recommendations based on similarities between titles tended torecommend very old books. The year of publication could be incorporated into the SVM as a feature in the future.
8. Conclusions
We proposed a method for utilizing multiple sources of information with an SVM and showed its effectiveness.
While the information that we used was limited to library loan records, book titles, NDC categories, and outlines
from the BOOK Database, other information could be easily added or incorporated. There are many possibilities
for expanding our method.
We showed that methods that use multiples sources of information with the SVM such as “NDC + Title + Loan
Record” and “Title + Loan Record” perform better than other methods that rely only on loan records or similarities
between titles and also better than methods than rely on other combinations of information. Therefore, library loan
records should be considered as a possible source of information when creating book recommendations eventhough there is a risk of privacy leakage associated with using them for this purpose. It was also indicated in thisstudy that relatively high evaluations could be obtained by recommending books with different NDC categories tostudents at the fourth-year undergraduate and graduate levels. Therefore, it is advisable to consider switching
recommendation methods depending on the target users.
The first task for the future is to determine how much the performance of the methods in this study will improvewhen the size of the learning data set increases. The possibility of incorporating information specific to theuniversity students into the book recommendations should also be pursued. This could include the courses andrelated reference books for each student. In addition, subjects were only asked to indicate the “degree of interest” inthis study, without any consideration for what they felt when they answered that they were “interested” in aparticular book. For instance, a subject might have replied that they were “interested” when a book wasrecommended from an unexpected new field, or because they knew about the book, but had not read it yet. We
hope to typify interests in the future in order to develop methods for book recommendations that are suitable foreach interest. In conclusion, this study only covered the SVM as a representative method for automaticclassification, while random forest methods and Naive Bayes classifier methods are also available for automaticclassification. It seems likely that these methods will not significantly surpass SVM’s performance, but comparisonand verification will be necessary in the future.
References
BOOK Database <http://www.nichigai.co.jp/dcs/index3.html>. [Accessed: July 5, 2013]
Chen, C., & Chen, A. (2007). Using data mining technology to provide a recommendation service in the digital library. The Electronic Library,25(6), 711-724.
Harada, T. (2009). The book recommendation system using library loan records. Digital Libraries, 36, 22-31 (text in Japanese).
Harada, T., & Masuda, K. (2010). A trial approach of weighting for library loan records for developing a book recommendation system. Digital
Libraries, 38, 54-66 (text in Japanese).
Luo, Y., Le, J., & Chen, H. (2009). A privacy-preserving book recommendation model based on multi-agent. Proceedings of the 2009 Second
International Workshop on Computer Science and Engineering, 323-327.
LIBSVM and easy.py <http://www.csie.ntu.edu.tw/~cjlin/libsvm/>. [Accessed: July 5, 2013]
MeCab <https://code.google.com/p/mecab/>. [Accessed: July 5, 2013]
Resnick, P. et al. (1994). GroupLens: an open architecture for collaborative filtering of netnews. Proceedings of ACM 1994 Conference on
Computer Supported Cooperative Work, 175-186.
Shirgaonkar, S., Rajkumar, T., & Singh, V., (2010). Application of improved apriori in university library. International Conference andWorkshop on Emerging Trends in Technology (ICWET 2010), 535-540.
Tsuji, K. et al. (2011). Use of library loan records for book recommendation. Proceedings of the International Conference on Integrated
Information (IC-ININFO 2011), 2011. (No Pagination).
Tsuji, K. et al. (2012). Effectiveness of using library loan records for book recommendation. Toshokankai (Library World), 64(3), 176–189(text in Japanese).
Whitney, C., & Schiff, L. (2006). Melvil Recommender Project: developing library recommendation services. D-Lib Magazine, 12(2).
<http://www.dlib.org/dlib/december06/whitney/12whitney.html>. [Accessed: July 5, 2013]

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Table 1. Overall Results
A: Already
Bought or Read
Loan Record
5(
3.9 )
Title
5(
2.6 )
NDC + Title
3(
3.1 )Title + Loan Record
3(
3.1 )NDC + Title + Loan Record
6(
6.3 )BOOKDB + NDC + Title + Loan Record
6(
6.3 )BOOKDB0(
0.0 )Amazon16 ( 23.9 )2: Very
Interested
22 ( 17.2
34 ( 17.7
16 ( 16.7
23 ( 24.0
14 ( 14.6
12 ( 12.5
9 ( 13.6
22 ( 32.8

)
)
)
)
)
)
)
)

0: NotInterested
60 ( 46.9
88 ( 45.8
36 ( 37.5
35 ( 36.5
27 ( 28.1
33 ( 34.4
25 ( 37.9
2(
3.0

)
)
)
)
)
)
)
)

0: NotInterested
30 ( 53.6
39 ( 50.0
20 ( 47.6
11 ( 26.2
14 ( 33.3
16 ( 38.1
11 ( 45.8
1(
2.6

)
)
)
)
)
)
)
)

0: NotInterested
22 ( 47.8
29 ( 40.3
12 ( 33.3
13 ( 36.1
9 ( 25.0
12 ( 33.3
11 ( 36.7
0(
0.0

)
)
)
)
)
)
)
)

0: NotInterested
8 ( 30.8
20 ( 47.6
4 ( 22.2
11 ( 61.1
4 ( 22.2
5 ( 27.8
3 ( 25.0
1 ( 10.0

)
)
)
)
)
)
)
)

1: Interested
)
)
)
)
)
)
)
)

41 (
65 (
41 (
35 (
49 (
45 (
32 (
27 (

32.0
33.9
42.7
36.5
51.0
46.9
48.5
40.3

Total
)
)
)
)
)
)
)
)

128
192
96
96
96
96
66
67

Table 2. Results for Graduate Students
A: Already
Bought or Read
Loan Record
5(
8.9 )
Title
4(
5.1 )
NDC + Title
3(
7.1 )Title + Loan Record
3(
7.1 )NDC + Title + Loan Record
3(
7.1 )BOOKDB + NDC + Title + Loan Record
3(
7.1 )BOOKDB0(
0.0 )Amazon13 ( 34.2 )2: Very
Interested
6 ( 10.7
10 ( 12.84(
9.5
7 ( 16.7
4(
9.5
3(
7.1
2(
8.3
13 ( 34.2

1: Interested
)
)
)
)
)
)
)
)

15 (
25 (
15 (
21 (
21 (
20 (
11 (
11 (

26.8
32.1
35.7
50.0
50.0
47.6
45.8
28.9

Total
56
78
42
42
42
42
24
38

Table 3. Results for Fourth-Year Undergraduate Students
A: Already
Bought or Read
Loan Record
0(
0.0 )
Title
0(
0.0 )
NDC + Title
0(
0.0 )Title + Loan Record
0(0.0 )
NDC + Title + Loan Record
3(
8.3 )BOOKDB + NDC + Title + Loan Record
3(
8.3 )BOOKDB0(
0.0 )Amazon2 ( 10.5 )2: Very
Interested
7 ( 15.2
15 ( 20.8
7 ( 19.4
11 ( 30.6
5 ( 13.9
4 ( 11.1
4 ( 13.3
7 ( 36.8

1: Interested
)
)
)
)
)
)
)
)

17 (
28 (
17 (
12 (
19 (
17 (
15 (
10 (

37.0
38.9
47.2
33.3
52.8
47.2
50.0
52.6

)
)
)
)
)
)
)
)

Total
46
72
36
36
36
36
30
19

Table 4. Results for Second-Year Undergraduate Students
A: Already
Bought or Read
Loan Record
0(
0.0 )
Title
1(
2.4 )
NDC + Title
0(
0.0 )Title + Loan Record
0(0.0 )
NDC + Title + Loan Record
0(
0.0 )BOOKDB + NDC + Title + Loan Record
0(
0.0 )BOOKDB0(
0.0 )Amazon1 ( 10.0 )2: Very
Interested
9 ( 34.6
9 ( 21.4
5 ( 27.8
5 ( 27.8
5 ( 27.8
5 ( 27.8
3 ( 25.0
2 ( 20.0

1: Interested
)
)
)
)
)
)
)
)

9(
12 (
9(
2(
9(
8(
6(
6(

34.6
28.6
50.0
11.1
50.0
44.4
50.0
60.0

)
)
)
)
)
)
)
)

Total
26
42
18
18
18
18
12
10

Table 5. Results Based on Volume of Learning Data
A: Already
Bought or Read
Learning
Title + Loan Record0(
0.0 )Data NDC + Title + Loan Record5(
2.6 )Small BOOKDB + NDC + Title + Loan Record
4(
2.1 )Learning
Title + Loan Record
3(
3.1 )Data NDC + Title + Loan Record
6(
6.3 )Large BOOKDB + NDC + Title + Loan Record
6(
6.3 )

2: Very
Interested
23 ( 12.0
35 ( 18.2
37 ( 19.3
23 ( 24.0
14 ( 14.6
12 ( 12.5

1: Interested
)
)
)
)
)
)

56 (
64 (
66 (
35 (
49 (
45 (

29.2
33.3
34.4
36.5
51.0
46.9

)
)
)
)
)
)

0: NotInterested
113 ( 58.9
88 ( 45.8
85 ( 44.3
35 ( 36.5
27 ( 28.1
33 ( 34.4

Total
)
)
)
)
)
)

192
192
192
96
96
96