Cross-domain collaboration recommendation

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2012, Pages 1285-1293

  Tang, Jie ^a   Wu, Sen ^a   Sun, Jimeng ^b   Su, Hang ^a  

^a TSINGHUA UNIVERSITY   (China)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

collaboration recommendation; social influence; social network

Indexed keywords

COLLABORATION RECOMMENDATION; CROSS-DOMAIN; CTL MODEL; DATA SETS; DIFFERENT DOMAINS; INTERDISCIPLINARY COLLABORATIONS; PARAMETER-TUNING; RECOMMENDATION PERFORMANCE; SOCIAL INFLUENCE; SOCIAL NETWORKS; TARGET DOMAIN;

HIGHER ORDER STATISTICS;

DATA MINING;

EID: 84866020834     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2339530.2339730     Document Type: Conference Paper

References (36)

1. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. ACM Press, 1999.
2. M. Balabanović and Y. Shoham. Fab: content-based, collaborative recommendation. Commun. ACM, 40:66-72, March 1997.
3. K. Balog, L. Azzopardi, and M. de Rijke. Formal models for expert finding in enterprise corpora. In SIGIR'06, pages 43-55, 2006.
4. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 3:993-1022, 2003.
5. W. Buntine and A. Jakulin. Applying discrete pca in data analysis. In UAI'04, pages 59-66, 2004.
6. D. Chakrabarti and C. Faloutsos. Graph mining: Laws, generators, and algorithms. ACM Comput. Surv., 38(1):2, 2006.
7. H.-H. Chen, L. Gou, X. Zhang, and C. L. Giles. Collabseer: a search engine for collaboration discovery. In JCDL'11, pages 231-240, 2011.
8. A. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: Scalable online collaborative filtering. In WWW'07, 2007.
9. M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst., 22(1):143-177, Jan. 2004.
10. A. Doucet, N. de Freitas, K. Murphy, and S. Russell. Rao-blackwellised particle filtering for dynamic bayesian networks. In UAI'00, pages 176-183, 2000.
11. M. Faloutsos, P. Faloutsos, and C. Faloutsos. On power-law relationships of the internet topology. In SIGCOMM'99, pages 251-262, 1999.
12. M. Granovetter. The strength of weak ties. American Journal of Sociology, 78(6):1360-1380, 1973.
13. T. L. Griffiths and M. Steyvers. Finding scientific topics. In PNAS'04, pages 5228-5235, 2004.
14. G. Heinrich. Parameter estimation for text analysis. Technical report, University of Leipzig, Germany, 2004.
15. T. Hofmann. Probabilistic latent semantic indexing. In SIGIR'99, pages 50-57, 1999.
16. H. Kautz, B. Selman, and M. Shah. Referral web: Combining social networks and collaborative filtering. Communications of the ACM, 40(3):63-65, 1997.
17. I. Konstas, V. Stathopoulos, and J. M. Jose. On social networks and collaborative recommendation. In SIGIR'09, pages 195-202, 2009.
18. J. Leskovec and C. Faloutsos. Sampling from large graphs. In KDD'06, pages 631-636, 2006.
19. J. Leskovec, D. Huttenlocher, and J. Kleinberg. Predicting positive and negative links in online social networks. In WWW'10, pages 641-650, 2010.
20. D. Liben-Nowell and J. M. Kleinberg. The link-prediction problem for social networks. JASIST, 58(7):1019-1031, 2007.
21. R. Lichtenwalter, J. T. Lussier, and N. V. Chawla. New perspectives and methods in link prediction. In KDD'10, pages 243-252, 2010.
22. L. Lovasz. Random walks on graphs: A survey. Combinatorics, 2(1):1?6, 1993.
23. D. Mimno and A. McCallum. Expertise modeling for matching papers with reviewers. In KDD'07, pages 500-509, 2007.
24. J. Quackenbush. Microarray analysis and tumor classification. New England Journal of Medicine, 354:2463-2472, June 2006.
25. D. Sculley. Combined regression and ranking. In KDD'10, pages 979-988, 2010.
26. Y. Shi, D. Ye, A. Goder, and S. Narayanan. A large scale machine learning system for recommending heterogeneous content in social networks. In SIGIR'11, pages 1337-1338, 2011.
27. M. Steyvers, P. Smyth, and T. Griffiths. Probabilistic author-topic models for information discovery. In KDD'04, pages 306-315, 2004.
28. J. Sun, H. Qu, D. Chakrabarti, and C. Faloutsos. Neighborhood formation and anomaly detection in bipartite graphs. In ICDM'05, pages 418-425, 2005.
29. J. Tang, L. Yao, and D. Chen. Multi-topic based query-oriented summarization. In SDM'09, pages 1147-1158, 2009.
30. J. Tang, J. Zhang, R. Jin, Z. Yang, K. Cai, L. Zhang, and Z. Su. Topic level expertise search over heterogeneous networks. Machine Learning Journal, 82(2):211-237, 2011.
31. J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su. Arnetminer: Extraction and mining of academic social networks. In KDD'08, pages 990-998, 2008.
32. L. Tang and H. Liu. Relational learning via latent social dimensions. In KDD'09, pages 817-826, 2009.
33. W. Tang, J. Tang, T. Lei, C. Tan, B. Gao, and T. Li. On optimization of expertise matching with various constraints. Neurocomputing, 76(1):71-83, 2012.
34. C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In KDD'11, pages 448-456, 2011.
35. Q. Yuan, L. Chen, and S. Zhao. Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation. In RecSys'11, pages 245-252, 2011.
36. J. Zhang, J. Tang, and J. Li. Expert finding in a social network. In DASFAA'07, pages 1066-1069, 2007.