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1 Using Social Recommendation in Academic Community

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2

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4 (Information Overload)

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5

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6

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7 (Social Network Analysis) [40]

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8 http://en.wikipedia.org/wiki/Social_network

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9 (Cont.) [21] Degreenumber of direct connections Betweennessrole of broker or gatekeeper Closeness Centralitywho has the shortest path to all others

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10 Clustering Algorithm Partitioning methods k-Means Hierarchical methods Agglomerative Divisive Model-based methods Self-Organizing Map

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11 Clustering Algorithm ( ) Partitioning methods k-Means Hierarchical methods Agglomerative Divisive Model-based methods Self-Organizing Map

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12 Clustering Algorithm ( ) Partitioning methods k-Means Hierarchical methods Agglomerative Divisive Model-based methods Self-Organizing Map

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13 (Content-based) (Collaborative Filtering)

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14 The vector model ranks the documents according to their degree of similarity to the query, and retrieve the documents with a degree of similarity above a threshold Define Weight w i,j associated with a pair (k i, d j ) is positive and non- binary (t is the total number of index terms) The index terms in the query are also weighted w i,q is the weight associated with the pair [k i, q], where w i,q >= 0 (t is the total number of index terms) Degree of similarity of d j with regard to q: The cosine of the angle between the two corresponding vectors

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15 Normalized Term-document matrix

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16

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17

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18 [38] (Title) (Abstract) (Keyword) (Author) http://ir.lib.nctu.edu.tw

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19 (Tokenization) (Lowercasing) (Stopword Removing) (Part-of-speech) (Chunking) (Stemming) (Feature Selection)

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20 Some combinatorial characteristics of matrix multiplication on regular two-dimensional arrays are studied. From the studies, the authors are able to design many efficient varieties of the cylindrical array and the two-layered mesh array for matrix multiplication. some combinatorial characteristics of matrix multiplication on regular two-dimensional arrays are studied from the studies the authors are able to design many efficient varieties of the cylindrical array and the two-layered mesh array for matrix multiplication combinatorial characteristics matrix multiplication regular two- dimensional arrays studied studies authors design efficient varieties cylindrical array two-layered mesh array matrix multiplication combinatorial_jj characteristics_nns matrix_nn multiplication_nn regular_jj two-dimensional_jj arrays_nns studied_vbn studies_nns authors_nns design_vb efficient_jj varieties_nns cylindrical_jj array_nn two-layered_jj mesh_nn array_nn matrix_nn multiplication_nn POSPhrase noun verb noun verb noun combinatorial characteristics matrix multiplication regular two-dimensional arrays studied studies author design efficient varieties cylindrical array two-layered mesh array matrix multiplication POSPhrase noun verb noun verb noun combinatori characterist matrix multipl regular two-dimension arrai studi author design effici varieti cylindr arrai two-lay mesh arrai matrix multipl ( )

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21

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22 TF-IAF (Term Frequency-Inverse Author Frequency)[30] TF-IAF

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23 (Title) (Keyword)

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24 TF-IAF [9]

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25

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26 [9]

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27 Finding vertices whose weights are larger than the average weight

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28 (Cont.) k-Nearest Neighbor Approach[19] k (3-6) (3-6)

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29 Use k-nearest neighbor graph approach

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30 (Cont.) (Inter-connectivity) (Relative Inter- connectivity) (3-7) (3-7)

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31

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32 (Cont.) (3-8) (3-8)

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36

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37 ( )

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38 Nm U N m R U U ( R )

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39 ( ) (Cosine Similarity)

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40 [9] (Collaborative Filtering) n ( ) n

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41

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42

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43

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44 ( )

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45 ( )

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46 (Cont.)

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47 ( )

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48 (Precision) (Recall) [15]

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49 Class labelCluster label Network Communication Mobile Computing Routing Protocol PIM-SM Bandwidth Requests TCP Network Management Artificial Intelligence Genetic Algorithm Network Motif Brick Motif Content Analysis Neural Network SPDNN Divide-and-conquer Learning Computer Graphics Content-based Image Retrieval Watershed Segmentation Toboggan Approach Information Retrieval Semantic Query Content Management Computer System Memory Cache Parallel Algorithm Information SecurityEnd-to-end Security Graph TheoryInterconnection Network Software EngineeringReliability Analysis ( )

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50 ( ) Class label# of authors Network Communication111 Artificial Intelligence28 Information Retrieval7 Computer System6 Computer Graphics23 Information Security10 Graph Theory29 Software Engineering4 Others17 Total235

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51 ( ) value00.10.20.30.40.50.60.70.80.91 Precision0.70710.69170.69810.71070.71720.7209 Recall0.62710.76060.77850.78390.78170.7828

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52 0.068 95% (0.632, 0.897) Kappa 0.764 0.95 208 187 187/208=0.899 Expert A NoYesTotal Expert B No 21 (9.6%)9(4.1%) 30 (13.7%) Yes2(0.9%) 187 (85.4%) 189 (86.3%) Total23(10.5%) 196 (89.5%)219

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53 1 41 1 129 55% 5 93%

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54 ( ) NamePublications Yu-Chee Tseng ( ) Jimmy J. M. Tan ( ) Lih-Hsing Hsu ( ) Yi-Bing Lin ( ) Ying-Dar Lin ( ) Ling-Hwei Chen ( ) Chuen-Tsai Sun ( ) Jang-Ping Sheu ( ) Hsin-Chia Fu ( ) Hao-Ren Ke ( ) Wei-Pang Yang ( ) Wen-Guey Tzeng ( ) Chien-Chao Tseng ( ) Tseng-Kuei Li ( ) Wen-Chih Peng ( ) Chang-Hsiung Tsai ( ) Deng-Jyi Chen ( ) Yuan-Cheng Lai ( ) 41 36 33 32 26 17 14 13 11 10 8 7 6

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55 1 6 6 3% 2 6 220 97%

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56 Yu-Chee Tseng

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57 RankDegreeBetweennessCloseness 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Yu-Chee Tseng Yi-Bing Lin Ying-Dar Lin Jimmy J. M. Tan Lih-Hsing Hsu Hsin-Chia Fu Jang-Ping Sheu Chien-Chao Tseng Chuen-Tsai Sun Hao-Ren Ke Ling-Hwei Chen Wei-Pang Yang Hsiao-Tien Pao Zen-Chung Shih Chang-Hsiung Tsai Jeu-Yih Jeng Yeong-Yuh Xu Deng-Jyi Chen Wen-Guey Tzeng Ming-Hour Yang 43 32 29 26 16 15 14 12 11 10 8 7 Yu-Chee Tseng Chien-Chao Tseng Yi-Bing Lin Ming-Feng Chang Ying-Dar Lin Wen-Chih Peng Jimmy J. M. Tan Lih-Hsing Hsu Chuen-Tsai Sun Hsin-Chia Fu Ling-Hwei Chen Jang-Ping Sheu Sunny S.J. Lin Hao-Ren Ke Chi-Fu Huang Wen-Guey Tzeng Shi-Chun Tsai Deng-Jyi Chen Zen-Chung Shih Wei-Pang Yang 2660.333 2180.500 2081.333 1792.000 376.500 340.000 213.167 133.167 91.000 86.000 44.000 38.333 36.000 32.833 22.500 21.333 15.333 12.000 8.833 Yu-Chee Tseng Chien-Chao Tseng Ming-Feng Chang Chi-Fu Huang Hsiao-Lu Wu Yuan-Ying Hsu Jung-Hsuan Fan Yi-Bing Lin Hang-Wen Hwang Jang-Ping Sheu Wen-Chih Peng Meng-Ta Hsu Lin-Yi Wu Ming-Hour Yang Chih-Yu Lin Sze-Yao Ni Wen-Hwa Liao Shih-Lin Wu Chih-Shun Hsu Chi-He Chang 0.678 0.677

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58

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59 235 226 22 0.899

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60

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61 ( ) Floyd-Warshall ( )

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62 ( ) 1.A. Iskold, (2007) The Art, Science and Business of Recommendation Engines. http://www.readwriteweb.com/archives/recommendation_engines.php 2.A. K. Jain, M. N. Murty, & P. J. Flynn, Data clustering: A review, ACM Computing Surveys, vol. 31, pp. 264-323, 1999. 3.B. Krulwich, & C. Burkey, The InfoFinder agent: Learning user interests through heuristic phrase extraction, IEEE Expert: Intelligent Systems and Their Applications, vol. 12, pp. 22-27, 1997. 4.B. Sarwar, G. Karypis, J. Konstan, & J. Riedl, Analysis of recommendation algorithms for e- commerce, Proceedings of the 2nd ACM conference on Electronic commerce, pp. 158-167, 2000. 5.D. Goldberg, D. Nichols, B. M. Oki, & D. Terry, Using Collaborative Filtering to Weave An Information Tapestry, Communications of the ACM, vol. 35, pp. 61-70, 1992. 6.D. Koller, & M. Sahami, Hierarchically classifying documents using very few words, Proceedings of 14th the International Conference on Machine Learning, pp.170178, 1997. 7.F. Sebastiani, Machine learning in automated text categorization, ACM Computing Surveys, vol. 34, pp. 1-47, 2002. 8.G.. Karypis, E. H. Han, & V. Kumar, Chameleon: Hierarchical clustering using dynamic modeling, Computer, vol. 32, pp. 68-75, 1999. 9.H. C. Chang, & C. C. Hsu, Using topic keyword clusters for automatic document clustering, Transactions on Information and Systems, vol. 88, pp. 1852-1860, 2005. 10.H. Hotta, User profiling system using social networks for recommendation, In Proceedings of 8th International Symposium on Advanced Intelligent Systems, 2007. 11.H. Kautz, B. Selman, & F. Park, Referral Web: Combining social networks and collaborative filtering, Communications of the ACM, vol. 40, pp. 63-65, 1997.

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63 ( ) 12.H. Sakagami, & T. Kamba, Learning Personal Preferences on Online Newspaper Articles from User Behaviors, Computer Networks and ISDN Systems, vol. 29, pp. 1447-1455, 1997. 13.J. B. Schafer, J. Konstan, & J. Riedi, Recommender systems in e-commerce, Proceedings of the 1st ACM conference on Electronic commerce, pp. 158-166, 1999. 14.J. MacQueen, Some methods for classification and analysis of multivariate observations, Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281-297, 1967. 15.J. Makhoul, F. Kubala, R. Schwartz, & R. Weischedel, Performance measures for information extraction, Proceedings of DARPA Broadcast News Workshop, pp. 249-252, 1999. 16.J. Moreno, Who Shall Survive? New York: National Institute of Mental Health, 1934. 17.J. R. Tyler, D. M. Wilkinson, & B. A. Huberman, Email as spectroscopy: Automated discovery of community structure within organizations, Communities and technologies, pp. 81-96, 2003. 18.J. Rucker, & M. J. Polanco, Siteseer: Personalized navigation for the web, Communications of the ACM, vol. 40, pp. 73-76, 1997. 19.K. C. Gowda, & G. Krishna, Agglomerative clustering using the concept of mutual nearest neighbourhood, Pattern Recognition, vol. 10, pp. 105-112, 1978. 20.K. Faust, Comparison of methods for positional analysis:Structural and general equivalences, Social Networks, vol. 10, pp.313-341, 1988. 21.L. C. Freeman, Centrality in Social Networks: Conceptual clarification, Social Networks, vol. 1, pp. 215-239, 1979. 22.L. Page, & S. Brin, The anatomy of a large-scale hypertextual Web search engine, In Proceedings of the seventh international World-Wide Web conference, 1998.

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64 ( ) 23.L. Garton, C. Haythornthwaite, & B. Wellman, (1997) Studying Online Social Networks, http://jcmc.huji.ac.il/vol3/issue1/garton.html 24.M. A. Shah, ReferralWeb: A resource location system guided by personal relations, Master's thesis, M.I.T., 1997. 25.M. Granovetter, The strength of weak ties: A network theory revisited, Sociology Theory, vol. 1, pp. 201-233, 1983. 26.N. Zhong, J. Liu & Y. Yao, In search of the wisdom web, Computer, vol. 35, pp. 27-31, 2002. 27.P. Athanasios, Probability, Random Variables and Stochastic Processes., Second Edition ed. New York: McGraw-Hill, 1984. 28.P. Mika, Flink: Semantic Web technology for the extraction and analysis of social networks, Web Semantics: Science, Services and Agents on the World Wide Web, vol 3, pp. 211-223, 2005. 29.P. Pattison, Algebraic models for social networks., Cambridge University Press, 1993. 30.S. E. Chan, R. K. Pon, & A. F. Crdenas, Visualization and Clustering of Author Social Networks, International Conference on Distributed Multimedia Systems Workshop on Visual Languages and Computing, pp. 30-31, 2006. 31.S. P. Borgatti, (1998) What Is Social Network Analysis? http://www.analytictech.com/networks/whatis.htm 32.S. Staab, P. Domingos, P. Mike, J. Golbeck, D. Li, T. Finin, A. Joshi, A. Nowak, & R. R. Vallacher, Social networks applied, IEEE Intelligent Systems, vol. 20, pp. 80-93, 2005. 33.V. Kotlyar, M. S.Viveros, S. S. Duri, R. D. Lawrence, & G. S. Almasi, A case study in information delivery to mass retail markets, In Proceedings of the 10th International Conference on Database and Expert Systems, 1999.

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65 ( ) 34.X. Liu, J. Bollen, M. L. Nelson, & H. V. de Sompel, Co-authorship networks in the digital library research community, Information Processing and Management, vol 41, pp. 1462-1480, 2005. 35.Y. Matsuo, J. Mori, & M. Hamasaki, POLYPHONET: An advanced social network extraction system from the Web ", Web Semantics: Science, Services and Agents on the World Wide Web, vol. 5, pp. 262-278, 2007. 36.Kappa Statistics - http://www.dmi.columbia.edu/homepages/chuangj/kappa 37.LingPipe NLP Toolkit - http://alias-i.com/lingpipe/ 38.NCTUIR - http://ir.lib.nctu.edu.tw/ 39.Porter Stemming Algorithm - http://tartarus.org/~martin/PorterStemmer 40.D. J. Watts , , , 6 , 2004. 41. , , 2001. 42. , , 2006. 43. , , 2007 , 423-434, 2007.