|
2. McPherson M, Lovin L S, Cook J M. Birds of a feather: homophily in social networks. Annual Review of Sociology, 2001, 27(1): 415-444
3. Yang S H, Long B, Smola A, et al. Like like alike: joint friendship and interest propagation in social networks. Proceedings of the20th International Conference on World Wide Web (WWW’11), Mar 28-Apr 1, 2011, Hyderabad, IA, USA. New York, NY, USA: ACM, 2011: 537-546
4. Lü L, Zhou T. Link prediction in complex networks: a survey. Physica A, 2011, 390(6): 1150-1170
5. Wang C, Satuluri V, Parthasarathy S. Local probabilistic models for link prediction. Proceedings of the 7th IEEE International Conference on Data Mining (ICDM’07), Oct 28-31, 2007, Omaha, NE, USA. Los Alamitos, CA, USA: IEEE Computer Society, 2007: 322-331
6. Lichtenwalter R N, Lussier J T, Chawla N V. New perspectives and methods in link prediction. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’10), Jul 25-28, 2010, Washington, DC, USA. New York, NY, USA: ACM, 2010: 243-252
7. Zhou T, Lü L, Zhang Y C. Predicting missing links via local information. The European Physical Journal B, 2009, 71(4): 623-630
8. Cranshaw J, Toch E, Hong J, et al. Bridging the gap between physical location and online social networks. Proceedings of the 2010 ACM Conference on Ubiquitous Computing (UBICOMP’10), Sep 26-29, 2010, Copenhagen, Denmark. New York, NY, USA: ACM, 2010: 119-128
9. Scellato S, Noulas A, Mascolo C. Exploiting place features in link prediction on location-based social networks. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’11), Aug 21-24, 2011, San Diego, CA, USA. New York, NY, USA: ACM, 2011: 1046-1054
10. Zhou T C, Ma H, Lyu M R, et al. UserRec: a user recommendationframework in social tagging systems. Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI’10),Jul 11-15, 2010,Atlanta, GA, USA. Menlo Park, CA, USA: AAAI, 2010: 1486-1491
11. Rendle S, Schmidt-Thieme L. Pairwise interaction tensor factorization for personalized tag recommendation. Proceedings of the 3rd ACM International Conference on Web Search and Data Mining (WSDM’10), Feb 4-6, 2010, New York, NY, USA. New York, NY, USA: ACM, 2010: 81-90
12. Symeonidis P, Nanopoulos A, Manolopoulos Y. Tag recommendations based on tensor dimensionality reduction. Proceedings of the 2nd ACM Conference on Recommender systems (RecSys’08), Oct 23-25, 2008, Lausanne, Switzerland. New York, NY, USA: ACM, 2008: 43-50
13. Symeonidis P, Nanopoulos A, Manolopoulos Y. A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis. IEEE Transaction on Knowledge and Data Engineering, 2010, 22(2): 179-192
14. Rendle S, Marinho L B, Nanopoulous A, et al. Learning optimal ranking with tensor factorization for tag recommendation. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09), Jun 28-Jul 1, 2009, Paris, France. New York, NY, USA: ACM, 2009: 727-736
15. Rendle S, Freudenthaler C, Gantner Z, et al. BPR: Bayesian personalized ranking from implicit feedback. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI’09), Jun 18-21, 2009, Montreal, Canada. Arlington,VA, USA: AUAI Press, 2009: 452-461
16. Cantador I, Brusilovsky P, Kuflik T. Second workshop on information heterogeneity and fusion in recommender systems (HetRec 2011). Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11), Oct 23-27, 2011, Chicago, IL, USA, New York, NY, USA: ACM, 2011: 387-388 |
