Decision Support in Library Book Acquisition: a Social Computing-based Approach

Fan Wu, Ya-Han Hu, Ping-Rong Wang, Feng-I Chung, Chia-Lun Lo Department of Information Management, National Chung Cheng University

168, University Rd., Min-Hsiung Chia-Yi 621, Taiwan, R.O.C. misfw@ccu.edu.tw, yahan.hu@mis.ccu.edu.tw, n223126@hotmail.com, albertchung@lib.ccu.edu.tw, ntallen1@gmail.com

Abstract—Due to the explosion of knowledge in the library, the librarians need to make effective decisions in book acquisition under the limited budget. Since librarians can not fully realize the needs of readers, many libraries provide the book recommendation service such that the readers can recommend the books to the libraries. However, the readers may only recommend the books in accordance with their own interest, resulting in the recommended books not meeting the needs of other readers. To facilitate decision-making in book acquisition, this paper used the social network to establish the relationships between the recommender and his/her related readers via the historical circulation records. Upon the social network, this paper uses social computing to evaluate the representative degree of the recommender, and then rank the recommended books. The retrospective and empirical studies are performed to show the effectiveness of the proposed ranking system by the spearman’s rank correlation coefficient. That is, the ranking of the recommended books by the proposed system is very like the two rankings of these books from the analysis of the historical circulation records and the librarian.

Keywords- book acquisition; social network; recommendation; social computing; library science

I. INTRODUCTION The purpose of a library is to provide the information for

its readers. In response to the explosion of knowledge, to buy new books for the needs of readers is an important task of the library. In order to enhance acquisition effectiveness of book and maximize the utility of resource under the constraint of the limited budget, the librarians must make appropriate decision in book acquisition for library. However, the acquisition may not always meet the needs of readers since the librarians can’t fully realize the needs of readers [1]–[3]. Accordingly, how to achieve maximum effectiveness in buying suitable books to satisfy the needs of readers is a significant issue in library science [4]–[8].

Though the library has the collection development policy for buying books, the library still can’t formulate an effective decision-making in book acquisition, especially for the condition that a lot of publishers publish a variety of books yearly. Then, Rashid [9] indicated that library tries to avert readers’ dissatisfaction in decision-making of book acquisition. In recent years, many researches had applied different technologies, including statistics mechanism [3], [5], mathematical model [8], linear goal programming [6], goal programming [7], and data mining [2], [5] and etc., to adopt some factors like the historical circulation records of books for decision-making in book acquisition. Generally, the circulations of books can be as a measure of appropriateness

in selecting books since it can reflect the needs of readers [7] and can be filed from the historical circulation records. The historical circulation records contain the reader’s name, the dates of borrowing, reserving, renewing, and return of books. Clearly, the historical circulation records can show the frequency of books in a certain category borrowed, reserving, and renewing by readers, and if decision-making in book acquisition considers the needs of readers, the circulations of new books are expected to achieve a very high level.

Note that the historical circulation records only represent the needs of readers for current books owned by the library, which can’t fully reflect the needs of readers for new books not bought by the library. To catch the needs of readers for new books, Ameen et al. [1] suggested that the librarians can encourage readers to recommend which books should be bought by the library. At present, many libraries have provided the book recommendation service for readers such that the library can choose new books from the recommended-book list. Nevertheless, the readers may only recommended books with the consideration of own needs or interest. The librarians can not ensure whether the recommended books meet the needs of other readers. Hence, how to evaluate and rank the books in the recommended-book list is an important issue of decision-making in book acquisition. To the knowledge of ours until now, there is no research focusing on evaluating the books in the recommended-book list for decision-making in book acquisition. To rank those recommended books, this paper first uses the social network to establish the relationships between the recommender and his/her related readers to evaluate the needs of readers for the recommended book.

This social network contains a set of nodes and the links between the nodes, in which the nodes represent the recommenders and other readers, and the links represent the relationships between the recommender and some other reader if they both borrowed the same books in the past. We only focus on the links between the recommender and his/her related readers. In detail, for a link between a recommender and a reader, the more same books they both borrowed, the stronger the link will be. Clearly, the strength of a link can represent the similarity of needs for the same books between the recommender and the reader. The number of links connects the recommender to other readers can be used to denote the authority (or representation) of the recommender in the needs of books. In other words, the more the number of the links connecting to the recommender to other readers are, the higher degree of the representation for the needs of books by the recommender will be.

Based on the social network for the recommenders and readers, this paper applies the social computing to develop a

2011 Eighth IEEE International Conference on e-Business Engineering

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ranking system in recommended book acquisition, which evaluates the representative degrees of the recommenders by the historical circulation records. The historical circulation records include the intralibrary and interlibrary loan records, where the former can denote the needs of internal readers for the books, while the latter denote the needs of external readers for the books.

This paper also considers the different needs of readers in different categories since the library generally advocates the balanced number of buying books in different categories based on the acquisition policy. However, the size of readership is not uniform in different categories, resulting in the large difference of the circulations of books between categories. If the needs of readers are not analyzed separately in different categories, the needs of readers in the category with low circulation will be neglected. Hence, this paper builds the social network for each category separately, and then evaluates the representative degree of the recommender in different categories. Finally, this paper uses the representative degrees of the recommenders in different categories to rank the recommended books.

In the library, the recommended books can be as a reference for decision-making in book acquisition since the recommended books may meet the needs of other readers. In this paper, we establish the relationships between the recommender and his/her related readers based on the social network and apply the social computing to evaluate the representative degree of the recommender. Then, the recommended books are ranked according to its representative degree of the recommender. We performed the retrospective and empirical studies to show the effectiveness of the proposed ranking system. The spearman's rank correlation coefficients show that the ranking of the proposed system is similar the two ranking of readers’ circulation and the librarian for the recommended books. That is, the proposed ranking system is effective for decision-making in book acquisition.

II. RELATED WORK

A. Decision-making of book acquisition Shieh and Wei [3] considered qualitative and quantitative

factors that affect the decision-making in book acquisition. Since qualitative factors are difficult to convert into numerical data, the majority of library-related researches adopted quantitative factors for decision-making. Quantitative factors, especially the historical circulation records, can easily predict the needs of readers. Thus, Shieh and Wei [3] applied quantitative factors as the basis of decision-making in book acquisition through using the cross-tabulation analyses upon the historical circulation records that form a multidimensional cube. Furthermore, Wu [10] pointed out that the historical circulation records are suitably adopted for decision-making in book acquisition.

At the present time, there are many researches applying statistics mechanism [3], [5], mathematical model [8], linear goal programming [6], and goal programming [7], to analyze the historical circulation records for decision-making in book acquisition. Wu et al. [5] evaluated the circulation of books

in each category from the historical circulation records with statistics mechanism. They also explored the association relationships between categories by mining association rules to predict the needs of readers. Li et al. [8] developed a decision support system of book acquisition with the mathematical models based on the estimation of books in each category in the viewpoint of the values of the books and the needs of readers. Wise et al. [6], [7] analyze the factors of the historical circulation records via goal programming as a suggestion in book acquisition. They list the constraints of the situation, such as the limited budget, the circulations of the books and so on, to find the numbers of books to be bought in each category. Furthermore, Wise et al. [7] showed that linear goal programming is effective enough in assisting decision-making in book acquisition.

Some other researches have adopted data mining [2], [5] to derive the needs of readers from the historical circulation records. Data mining can discover implicit knowledge and relationship in large databases through different mining technologies, such as clustering, classification, association, text mining and so on. Sitanggang et al. [2] extracted frequently-borrowed book sequences of readers in the historical circulation records via sequential pattern mining such that the needs of readers are considered in the book acquisition. From the previous researches, it can be seen that data mining is a useful technology to support decision-making in book acquisition, and that the historical circulation records can reflect the needs of readers for the purchased books. However, There are no historical circulation records of the recommended books for analyses. Thus, the representative degree of a recommender is an important factor in considering whether to buy the books recommended by him/her. In this paper, we first use the social network to establish the relationships between the recommender and his/her related readers and then apply the social computing for calculating the representative degree of the recommender.

B. Social network and social computing Social network is a sociogram showing the interplay

relationships between actors via network structure. The sociogram is composed by the actors (denoted as nodes) and the relationships between the actors (denoted as the links between the nodes). In social network, there are two important attributes associated with a link: namely, content and strength [11]. In this paper, the content of a link between a recommender and his/her related reader denotes their needs for the same books; while the strength of a link denotes the similarity of needs for same books that the recommender and that reader both borrowed in same category. Since this paper only needs to evaluate the recommended books recommended by a recommender, the representative degree of this recommender is the major concerned. Thus, the exploration of the whole network [11] between all the readers can be reduced to the egocentric network of a recommender. The egocentric network [11] is a part of whole network from a view of a certain actor in the network. The egocentric network of a recommender used in this paper is to reveal the representative degree of the recommender for other readers related to the recommender.

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Figure 1. An egocentric network of the recommender 1 and 2 for

category A

For example, Figure 1 is an egocentric network of the recommender1 and 2 for the category A, where six other readers borrowed the same books as recommender1; while four other readers borrowed the same book as recommender2. Note that the number (i.e. four) of readers borrowed the same books as recommender2 is smaller than the number (i.e. six) of readers borrowed the same books as recommender1. However, the recommender2 may be more representative for the four related readers than recommender1 for the six related readers since the former four readers borrowed more the same books than the latter six readers. Therefore, the representative degree of a recommender considers not only the number of the related readers but also the historical circulation records of the related readers.

Social computing is a social sciences field with computing system to obtain the social interactions and social behavior by quantifying the data about the interaction of nodes [12]–[14], and to reveal the potential relevance of influencing factors with externalized information [15]. Social computing can be applied to the business and public area [14]. For example, Wu et al. [16] used the social computing, proposed a trust-evaluation system in auction websites to identify the malicious participant in order to reduce the risk of cheats by participants. Since the recommenders and other readers can form a social network, this paper applied the conception of social computing to develop a ranking system for evaluating the recommended books. The detailed description of the system is shown in next section.

III. METHOD To compute the representative degree of a recommender,

this paper first constructs the egocentric network for the recommender and the related readers through the analyses of the historical circulation records. The related readers to a recommender are defined as the readers who used to borrow the same books as the recommender in the category of the books recommended by the recommender. Intuitively, the borrowing of a book by readers implies the needs of readers for the book. The more the same books borrowed by the recommender and his/her related readers are, the more similarity of the needs for the book the recommender and the related readers have. If the needs of the recommender are very similar to his/her related readers, the related readers will have a higher probability as the recommender to need the books recommended by the recommender. Thus, the number of the same books borrowed by the recommender and his/her related reader can be as a factor to evaluate the needs of the related reader for the recommended books. Developed upon the egocentric network, this paper applied the social computing with the mathematical model to evaluate the representative degree of the recommender. In the following, we will derive the mathematical model step-by-step to

compute the representative degree of the recommender and then rank the recommended books.

A. Importance of books in a category The borrowing, reserving, and renewing behaviors of

readers can reveal the needs of readers for a book. Thus, the more readers borrowed, reserved, and renewed a book, the greater the importance of the book is. We can accumulate the numbers of readers who borrowed, reserved, and renewed the book to evaluate the importance of a book in a category. Since the readers have a variety of interests in the books of different categories, the above accumulated numbers should be categorized at first. Furthermore, the borrowing, reserving, and renewing behaviors of readers are different in different categories, the importance of a book should be counted on the three ratios of the numbers of the readers who borrowed, reserved, and renewed the book against the total numbers of readers who borrowed, reserved, and renewed any books.

Definition 1: Assume that there is a set of w books, in the category K, and that for a book, say K

ib , there are KiBR , K

iRR , and K

iRE readers who respectively borrowed, reserved, and renewed book K

ib in the category K in the period from the registration date of book K

ib to the current date. Then the three ratios of book K

ib are expressed as follows: ;BRBRRaBR w

1jKj

Ki

Ki � �� ;RRRRRaRR w

1jKj

Ki

Ki � �� .RERERaRE w

1jKj

Ki

Ki � ��

Example 1: Suppose that the numbers of readers who borrowed, reserved, and renewed five books, d, e, f, g, and h in the category A from the registration date until a current date are given in Table I, and the numbers of readers who borrowed, reserved, and renewed eight books, l, o, p, q, r, s, t, and u in the category B from the registration date until a current date are also given in Table II. For a book A

db , there are 7, 3, and 2 readers to borrow, reserve, and renew the book. In addition, the numbers of total readers who borrowed, reserved, and renewed the books in category A are 57, 21, and 14. Then the three ratios, A

dRaBR , AdRaRR , and A

dRaRE , of book A

db are 0.12, 0.14, and 0.14, respectively. For another book B

lb , there are 7, 3, and 2 readers to borrow, reserve, and renew the book. Since the numbers of total readers who borrowed, reserved, and renewed the books in category B are 83, 31, and 18. Then those three ratios of book B

lb are 0.08, 0.10, and 0.11, respectively. It can be seen that though the numbers of readers who borrowed, reserved, and renewed the books A

db and Blb are the same, the importance of book

Blb is smaller than that of the book A

db since the three ratios of book B

lb are smaller than those of book Adb .

TABLE I. THE NUMBERS OF READERS IN THE CATEGORY A Behavior

Book Borrowing Reserving Renewing Adb 7 3 2 A

eb 10 5 2 Afb 15 6 3 Agb 6 2 2 Ahb 19 5 5

1

2 3

4 recommender2 recommender1

1 4

5 6 7

8

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TABLE II. THE NUMBERS OF READERS IN THE CATEGORY B Behavior

Book Borrowing Reserving Renewing Blb 7 3 2 Bob 7 3 1 Bpb 15 5 2 Bqb 6 2 3 Brb 8 6 1 Bsb 10 5 2 Btb 25 5 6 Bub 5 2 1

The above three ratios reveal the importance of a book in the past. The needs of readers may decay along with the passing of the time since the newest version of the same book or a higher quality of similar books may appear. To evaluate the decay of the importance of a book, the idle periods from the last dates to borrow, reserve, and renew a book until a current date is an indicator about the importance of a book. The longer the idle period is, the less importance the book is. The registration date for a book is another time factor affecting the importance of the book. Generally, if a book has been registered for a long time, the needs of readers for the book may decay since a new version or a variety of similar books may be bought into the library subsequently. If the active periods from the registration date to the last dates to borrow, reserve, and renew is large, it means that the book has a long life time to meet the needs of readers after its purchasing; or the importance of the book is still high. Therefore, the above two time periods can be used to adjust the importance measure of the book. Note that the above time factors are counted in the unit of week.

Definition 2: Let KiIBR , K

iIRR , and KiIRE be the idle

periods from the last dates to borrow, reserve, and renew book K

ib in category K to the current date, respectively, and let K

iABR , KiARR , and K

iARE be the active periods from the registration date to the last dates to borrow, reserve, and renew book K

ib in category K, respectively. The importance of book K

ib should be inverse proportional to the three idle periods, and be proportional to the three active periods. The time factors affecting the importance of books are summarized in the following lemma.

Lemma 1: Let the three time factors of book Kib in the

viewpoint of borrowing, reserving, and renewing behaviors are expressed as follows:

;Ki

Ki

Ki IBRABRTBR � ;K

iKi

Ki IRRARRTRR � .K

iKi

Ki IREARETRE �

Then the three importance measures, considering the time factors and the ratios of numbers of readers who borrowed, reserved, and renewed the book in category K, are expressed as follows:

,Ki

Ki

Ki TBRRaBRIpBR �� ,K

iKi

Ki TRRRaRRIpRR �� .K

iKi

Ki TRERaREIpRE ��

Example 2: (Continuing from Example 1) Assume that the active periods of borrowing, reserving, and renewing book

Adb are 17.2, 38.9, and 51.7. The idle periods of borrowing,

reserving, and renewing book Adb are 47.1, 25.4, and 12.6.

The three time factors of book Adb are 0.37, 1.53, and 4.10.

The importance of book Adb are 0.04, 0.21, and 0.57.

Different libraries may emphasize different behaviors of borrowing, reserving, and renewing. This paper assigns three weights, � , � , and � , where 1��� ��� . Thus, the three importance measures of book K

ib are revised as follows: ;K

iKi

Ki TBRRaBRIpBR ���� ;K

iKi

Ki TRRRaRRIpRR ��� � .TRERaREIpRE K

iKi

Ki ��� �

Most libraries provide the interlibrary loan service to readers such that the readers can use the books of other library. Thus, the readers in the library can broadly be divided into two types, i.e. the internal and external readers.

Definition 3: Assume that the three importance measures for book K

ib expressed as Ki

Ki

Ki ITBRIRaBRIIpBR ���� ,

Ki

Ki

Ki ITRRIRaRRIIpRR ��� � , and K

iKi

Ki ITREIRaREIIpRE ��� �

for the internal readers. Similarly, assume that the three importance measures for book K

ib expressed as Ki

Ki

Ki ETBRERaBREIpBR ���� , K

iKi

Ki ETRRERaRREIpRR ��� � , and

Ki

Ki

Ki ETREERaREEIpRE ��� � for the external readers.

Example 3: Assume that there are five books, d, e, f, g, and h, in category A. Table III shows the numbers of the internal and external readers who borrow, reserve, and renew the books in category A. The idle periods of borrowing, reserving, and renewing behaviors of the internal and external readers in the category are shown in Table IV, while the corresponding active periods are all shown in Table V. For book A

db , Assume the weights for � , � , and � are set as 0.2, 0.6, and 0.2, respectively. Then the three final importance measures for book A

db in the viewpoint of the internal and external readers, A

dIIpBR , AdIIpRR , A

dIIpRE , AdEIpBR , A

dEIpRR , and AdEIpRE , are 0.01, 0.10, 0.10, 0.004,

0.11, and 0.08.

TABLE III. THE NUMBERS OF THE INTERNAL AND EXTERNAL READERS ABOUT THREE BEHAVIORS IN THE CATEGORY A

Behavior Book

Internal readers External readers Borrowing Reserving Renewing Borrowing Reserving Renewing

Adb 5 2 1 2 1 1 A

eb 7 4 1 3 1 1 Afb 10 4 2 5 2 1 Agb 3 1 1 3 1 1 Ahb 12 2 3 7 3 2

TABLE IV. THE IDLE PERIODS IN THE CATEGORY A

Behavior Book

Internal readers External readers Borrowing Reserving Renewing Borrowing Reserving Renewing

Adb 47.1 30.2 12.6 52 25.4 19.3 A

eb 7.8 12.1 9 31.1 17.8 29.2Afb 79 97.2 81 85 101.7 77Agb 21.3 24 33 32.8 42.4 24.8Ahb 49.2 67 77 87.2 94.5 105.9

TABLE V. THE ACTIVE PERIODS IN THE CATEGORY A

Behavior Book

Internal readers External readers Borrowing Reserving Renewing Borrowing Reserving Renewing

Adb 17.2 34.1 51.7 12.3 38.9 45A

eb 28.2 23.9 27 4.9 18.2 6.8Afb 77 58.8 75 71 54.3 79Agb 55.7 53 44 44.2 34.6 52.2Ahb 191.8 174 164 153.8 146.5 135.1

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For a recommender, we can consider the recommender as important are if he/she always borrows the important books but also many other readers borrow many of the same books as the recommender.

B. Strength of a link between a recommender and a reader We build the egocentric network for each recommender

to calculate his/her representative degree through measuring the strengths of the links between the recommender and his/her related readers. By analyzing the historical circulation records, we use a set of vectors to represent the relationships between the recommender and the related readers.

Definition 4: Assume that recommender CM and one of his/her related readers, denoted as RD, both borrowed a set of z books in category K, denoted as KS . For a book, say K

vb , let K

vNBR , KvNRR , and K

vNRE be the numbers of times that reader RD has borrowed, reserved, and renewed book K

vb until current date, respectively. Then the set of the historical circulation records about the books in category K borrowed both by recommender CM and reader RD, is expressed as follows: �

KSv

Kv

Kv

Kv

Kv

KRD CM, )}NRE ,NRR ,NBR ,b{(CV

� .

Example 4: Suppose that there is a recommender CM recommending books in category A and that there are four related readers, RD1, RD2, RD3, and RD4 (RD4 is a external reader, the others are the internal readers.) who borrowed the same books A

db and Ahb as recommender CM. The links

between recommender CM and the four related readers are shown in the egocentric network in Figure 2. The set of vectors, {( A

db ,2,1,0)}, attached to node RD3, denotes that reader RD3 borrows the books A

db as recommender CM and that the numbers of times to borrow, reserve, and renew two books are 2,1,0.

Figure 2. The egocentric network of recommender CM

Clearly, the egocentric network of a recommender can show behaviors of all related readers for the books in a category. The degree that the related readers sympathize with the recommender can be quantified by the times of borrowing, reserving, and renewing the book. A related reader borrowed (reserved and renewed) c times the book as a recommender, the degree that the reader sympathizes with the recommender is c times longer than another reader who borrowed (reserved and renewed) one time. That is, the strength of the link between the former reader and the recommender is stronger the strength of the link between the latter reader and the recommender. The representative degree of a recommender is also influenced by the importance of books borrowed by them. If many related readers borrow the same books of higher importance as the recommender, the representative degree of the recommender will be high. Thus, the strength of the link between a related reader and the recommender is equal to the summation of all the multiplications of each circulation vector of books borrowed

by the reader and the recommender and the importance of those books.

Lemma 2: Assume that the strength of the link between recommender CM and internal and external reader RD are expressed as follows:

�� � ������ z1v

Kv

Kv

Kv

Kv

Kv

Kv IIpREREINIIpRRINRRIIpBRINBR)RD,CM(ILink ,

�� � ������ z1v

Kv

Kv

Kv

Kv

Kv

Kv IpREERENEIpRRENRREIpBRENBRE)RD,CM(ELink .

Example 5: (Continuing from Example 3 and 4) The internal importance measures of book A

db in the viewpoint of borrowing, reserving, and renewing behaviors are 0.01, 0.10 and 0.10, respectively. For internal reader RD3, circulation vector A

RD CM, 3BV of reader RD3 is {( A

db ,2,1,0)}. The strength of the link between recommender CM and internal reader RD3 is 0.12. The strengths of the links between the recommend and the related readers are summarized in Table VI.

TABLE VI. THE STRENGTHS OF THE LINKS BETWEEN THE RECOMMENDER AND THE RELATED READERS

RD1 0.13 RD2 1.06 RD3 0.12 RD4 0.02 Since the external readers may be difficult more than the

internal readers to borrow, reserve, and renew a book. This paper gives different weights, � and , to the strengths of the links of the internal and external readers, where � � and

1�� � , Finally, we add up the strengths of the links between the recommender and the internal and external readers to denote the representative degree of the recommender.

Lemma 3: Assume that there are two sets of n internal readers and m external readers borrowed the same book(s) as recommender CM in the category K. Let K

xRD and KyRD be

one of internal and external readers, respectively. The representative degree, denoted is expressed as follows:

� .)RD,CM(ELink)RD,CM(ILinkCMRepres m

1yKy

n

1xKxK �� ��

���� �

Example 6: (Continuing from Example 5) Assume the weights for � and , to the strengths of the links of the internal and external readers, are set as 0.2, 0.8, respectively. The representative degree �CMRepresA of recommender CM is 0.28 � �02.00.812.006.113.02.0 ������ . The ranking of the recommended books is counted on the representative degrees of the recommenders.

IV. RESULT AND DISCUSSION

A. Data set and preprocessing We get the recommended-book list in 2008 and perform

the historical circulation records from Sep., 1999 to Dec., 2010 in the Aleph system. We use some criteria to filter data. The filtering criteria are (1) the record in the viewpoint of reserving behaviors needs to be removed if the record was cancelled; (2) ISBN of the recommended book is irregular, which is not 10 or 13 digits; (3) the recommender did not borrow the books in the past, preventing the invalid evaluation for the recommender.

B. Empirical studies We use the spearman's rank correlation coefficient to

evaluate the correlation coefficients for three rankings of the

{( Adb , 1, 1, 1),( A

hb , 1, 2, 2)}

{( Adb ,2,1,0)} {( A

hb ,1,0,0)}

{( Ahb ,1,1,0)} RD2

RD3 RD4

RD1 CM

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proposed system, readers’ circulation, and the librarian in pairs. Then we perform the two groups to evaluate the correlation coefficients. One group is the two rankings of the proposed system and readers’ circulation as Group 1. The historical circulation records of the recommended book are quantified a score (i.e. readers’ circulation), which sum up the numbers of times that the readers borrowed, reserved and renewed the recommended book from the registration date until two years later. Another group is the two rankings of the proposed system and the librarian as Group 2.

We get the recommended books in a non-professional subcategory with the western to perform the retrospective and empirical studies. The subcategory contains the general, English, American literature, Fiction and Juvenile Belles Lettres (Call numbers are PN, PR, PS, and PZ, respectively.) in the language and literature. In Figure 3, we can find the books with the larger difference of two ranking of the proposed system and readers’ circulation are, PS3569.T33828 S814 2008, PR9265.9.C55 C637 1996, and PN6071.M7 C533 2008. The reason of first one is that readers borrowed the recommended book are related to recommender in the egocentric network, resulting in the ranking of readers’ circulation is high. The reason of last two books is that the situations of borrowing, reserving, and renewing the books in PR9265.9.C55 and PN6071.M7 subcategories are not good in the past such that the ranking of readers’ circulation is low. The correlation coefficient of Group 1 is larger than Group 2 in the language and literature. That is, the ranking of the proposed system is closer the needs of readers than the ranking of the librarian since this paper not only considers the borrowing, reserving, and renewing behaviors of readers but also the importance of a book with the passing of the time.

Figure 3. Three rankings in the language and literature

V. CONCLUSION Since many publishers publish a variety of books, the

library needs to refer information, like the recommended-book list, assisting the librarian to select the recommended book under the limited budget. In addition to adopt the borrowing, reserving, and renewing behaviors for the internal and external readers, we first use the social network to establish the relationships between the recommender and

his/her related readers, and then evaluating the representative degree of the recommender by social computing. The retrospective and empirical studies show that spearman's rank correlation coefficient is at least moderate positive correlation. That is, the proposed ranking system can evaluate the needs of readers for the recommended books to assist decision-making in book acquisition for library. In the future, the needs of readers are derived more and more by their other behaviors, the result of the proposed ranking system is more accurate.

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