The document proposes a social model for literature access that incorporates a weighted social network of authors. It presents a generic social information retrieval model involving information producers, documents, social annotations and relationships. For literature access specifically, it extracts a social network of authors based on co-authorships and citations. This network is weighted based on factors like co-authorship similarity and influence of citations. Author importance is then computed using measures like PageRank to derive a document's social score. The model is evaluated on ACM SIGIR publications, outperforming baselines that don't incorporate the weighted social network. Future work involves integrating additional social features and evaluating on more datasets.

1. A Social Model for literature Access: Towards a weighted social network of authors Lamjed Ben Jabeur, Lynda Tamine and MohandBoughanem IRIT, University of Paul Sabatier, Toulouse {jabeur,tamine,bougha}@irit.fr 1

2. A social model for Literature access: Towards a weighted social network Overview Towards Social Information Retrieval A Generic Social Information Retrieval Model A Social Model for Literature access Experimental evaluation Conclusion and future work 2

3. 1. Towards Social Information Retrieval Model 1. Social Information Retrieval IRS Query Tag Comment 3 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

4. 1. Towards Social Information Retrieval Model 1. Social Information Retrieval Information Producer Documents Social Information Retrieval Information Consumer Social Annotations R(q,d,G) R(q,d) 4 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

6. Access to relevant information by exploring the social network

7. Estimate document relevance from the social context

8. Crossing two domains

9. Information Retrieval

10. Represent and compare document /query

11. Social Network Analysis

12. Represent social entities and relationships

13. Estimate individual’s centralityRelevant information is related to importantpersons 5 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

15. Member and group similarity [Spertus, 2005]

16. Collaborative Production

17. Quality, authority (people/content) [korfatis, 2006] [Hu, 2007] [Wilkonson, 2007]

18. Knowledge sharing, transfer [Kang, 2009] [Agostini, 2003] [Hassen, 2002]

19. Finding innovation and creativity networks [Goyal, 2008]

20. Social recommender systems

21. User/ document similarity [Jamali, 2009] [Ma, 2009] [McDonald, 2009]

22. Collaborative filtering [Konstas, 2009] [Siersdorfer, 2009] [Nakamoto, 2008] [Sen, 2009]6 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

24. Tags suggestion [Budura, 2008] [Heymann, 2008] [Qu, 2008]

25. Folksonomy[Ding,2009] [Soledad,2009]

26. Expert search

27. Modeling expert network [Balog, 2006] [Karimzadehgan, 2009]

28. Expert finding [Fu,2007] [Zhang,2008]

29. People Search [Artiles, 2008] [Popescu, 2007]

30. Web Search

31. Modeling social information network [Amer-Yahia, 2007]

32. Social ranking and filtering [Jeh, 2002] [Xue, 2007]

33. Real-Time Search [Sankaranarayanany, 2009]

34. Literature Access [Mutschke, 2001] [Kirsch, 2006] [Yan, 2009]7 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

36. Information producing and consuming

37. Actors, data, relationships

38. Combine social relevance features

39. Specific social information for literature access

40. Social network topology

41. New entities: users and social annotations

42. New social relationships: Citations links between authors, tagging

43. A weighting model for social relationships

44. Evaluate scientific papers importance

45. Apply social importance measures on the social network of authors

46. Compare social importance measures 8 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

48. Authors, integrators

49. Users, annotators

50. Data

51. Documents

52. Social annotations

53. Social relationships

54. Implicit/Explicit

55. Directed/Undirected9 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

57. Social distance

58. Incoming links and bookmarks

59. Timeliness and freshness

60. Social importance of individuals10 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

62. Q: Queries

63. G: Social information network

64. F: Modeling process of documents and queries

65. R(qi,dj,G): Ranking function that combines a subset of social relevance features11 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

66. 3. A Social model for literature access 1. The Social Information Network of bibliographic resources 12 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

68. A: Authors

69. Ea: Author-to-author relationships

70. Edges express:

71. Co-authorships

72. Citation13 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

74. Similarity and shared interest between authors

75. Citation link

76. Influence and knowledge transfera1 a4 a2 a5 a4 a2 a5 a3 a3 a1 Co(1,2) With A(i,j) documents authored by aiand aj, A(i) documents published by ai Ci(1,4) C(i): citation announced by ai , C(I,j) number of time aicite aj 14 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

78. Author affiliation to the topic of the documentTd tags assigned to document d, Ak documents authored by ak, A documents authored by all d co-authors, Hkd tags information entropy and 1-θ default weight value. A Ak a1 W(a3,d) a3 d a2 15 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

80. Degree

81. Social activity

82. Popularity

83. Gregariousness

84. Closeness

85. Reachability|independence

86. Influence

87. Betweenss

88. Interdisciplinary

89. PageRank

90. Authority

91. Hits

92. Hub and authority16 16 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

93. 3. A Social model for literature access 5. Derive document social score CG(a3)) CG(a1)) CG(a3)) ImpG(d) CG(a2)) 17 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

95. Combine scores18 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

97. 2 053 documents

98. Social annotations gathered from CiteUlike1

99. 6 352 tag applications and 1 382 distinct tagsA: Co-authorships C: Citation links AC : Co-authorships and/or Citation links Social network proprieties The giant component [1] http://www.citeulike.org 19 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

101. Tags are user generated terms to annotate/index documents

102. Most frequently tags are more important in the social context

103. Relevance assumption: Strongly tagged documents

104. Tag is associated at least once to document

105. Tag is among the top-3 assigned tags to document

106. Measures

107. p@5

108. p@1020 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

110. Topical relevance

111. Select query relevant documents

112. PR-Docs model:

113. Combine topical relevance and document authority (in the citation graph).

114. Estimate the scientific importance of documents

115. Kirsch’s model:

116. Combine both topical and social relevance

117. Estimate social relevancefrom authors authority in the co-authorship networkα Tuning α for PR-Docs Model 21 21 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

118. 4. Experimental evaluation 4. Tuning the social model Binary social network α Tuning αparameter Weighted social network 22 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

120. W-Hub

121. α=0.9 (SM0.9)p@5 p@10 SM0.9 improvement Evaluation of the retrieval effectiveness 23 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

123. Include information consumer and social annotation

124. Include citation links between authors

125. Weight social relationships

126. Experimental evaluation

127. W-Hub better evaluate the scientific importance of bibliographic resources

128. Superiority of proposed model compared to traditional information retrieval and other closely related models24 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

130. Social distance

131. Popularity and freshness

132. Evaluate tag quality and information consumer centrality

133. Learn to rank with different social features

134. Experimental evaluation on larger scientific datasets with various research fields25 Towards SIR Model A Generic SIR Model A Social Model for LA Experimental evaluation Conclusion

135. Thanks for your attention!