[1] Chen T, Tang L, Liu Q et al. Combining factorization model and additive forest for collaborative followee recommendation. In Proc. 2012 KDD Cup Workshop, Aug. 2012.[2] Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer, 2009, 42(8): 30–37.[3] Salakhutdinov R, Mnih A. Probabilistic matrix factorization. In Proc. the 21st Annual Conference on Neural Information Processing Systems, Dec. 2007, pp.1257–1264.[4] Töscher A, Jahrer M, Bell R M. The BigChaos solution to the Netflix grand prize. 2009. http://www.netflixprize.com/assets/GrandPrize2009 BPC BigChaos.pdf, April 2015.[5] Hu L, Cao J, Xu G, Cao L, Gu Z, Zhu C. Personalized recommendation via cross-domain triadic factorization. In Proc. the 22nd International World Wide Web Conference, May 2013, pp.595–606.[6] Agarwal D, Chen B C. Regression-based latent factor models. In Proc. the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 28–July 1, 2009, pp.19–28.[7] Xu Z, Tresp V, Rettinger A, Kersting K. Social network mining with nonparametric relational models. In Proc. the 2nd Int. Conf. Advances in Social Network Mining and Analysis, Aug. 2008, pp.77–96.[8] Sharma A, Cosley D. Do social explanations work? Studying and modeling the effects of social explanations in recommender systems. In Proc. the 22nd International World Wide Web Conference, May 2013, pp.1133–1143.[9] Agarwal D, Chen B C. fLDA: Matrix factorization through latent dirichlet allocation. In Proc. the 3rd ACM International Conference on Web Search and Data Mining, Feb. 2010, pp.91–100.[10] McAuley J, Leskovec J. Hidden factors and hidden topics: Understanding rating dimensions with review text. In Proc. the 7th ACM Conference on Recommender Systems, Oct. 2013, pp.165–172.[11] Wang C, Blei D. Collaborative topic modeling for recommending scientific articles. In Proc. the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2011, pp.448–456.[12] Hofmann T. Probabilistic latent semantic indexing. In Proc. the 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Aug. 1999, pp.50–57.[13] Breese J, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. the 14th Conference on Uncertainty in Artificial Intelligence, July 1998, pp.43–52.[14] Cai Y, Leung H F, Li Q, Min H, Tang J, Li J. Typicalitybased collaborative filtering recommendation. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(3): 766–779.[15] Deshpande M, Karypis G. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems, 2004, 22(1): 143–177.[16] Liu N N, Yang Q. Eigenrank: A ranking-oriented approach to collaborative filtering. In Proc. the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2008, pp.83–90.[17] Ma H, King I, Lyu M R. Effective missing data prediction for collaborative filtering. In Proc. the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2007, pp.39–46.[18] Hofmann T. Collaborative filtering via Gaussian probabilistic latent semantic analysis. In Proc. the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 28-Aug. 1, 2003, pp.259–266.[19] Si L, Jin R. Flexible mixture model for collaborative filtering. In Proc. the 20th Annual International Conference on Machine Learning, Aug. 2003, pp.704–711.[20] Zhang Y, Koren J. Efficient Bayesian hierarchical user modeling for recommendation system. In Proc. the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2007, pp.47– 54.[21] Porteous I, Bart E, Welling M. Multi-HDP: A non parametric Bayesian model for tensor factorization. In Proc. the 23rd AAAI Conference on Artificial Intelligence, July 2008, pp.1487–1490.[22] Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann Machines for collaborative filtering. In Proc. the 24th Annual International Conference on Machine Learning, June 2007, pp.791–798.[23] Salakhutdinov R, Mnih A. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In Proc. the 25th Annual International Conference on Machine Learning, July 2008, pp.880–887.[24] Rennie J D, Srebro N. Fast maximum margin matrix factorization for collaborative prediction. In Proc. the 22nd Annual International Conference on Machine Learning, Aug. 2005, pp.713–719.[25] Yu K, Zhu S, Lafferty J, Gong Y. Fast nonparametric matrix factorization for large-scale collaborative filtering. In Proc. the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2009, pp.211–218.[26] Ma H, Zhou T C, Lyu M R, King I. Improving recommender systems by incorporating social contextual information. ACM Transactions on Information Systems, 2011, 29(2): Article No. 9.[27] Wu S, Sun J, Tang J. Patent partner recommendation in enterprise social networks. In Proc. the 6th ACM International Conference on Web Search and Data Mining, Feb. 2013, pp.43–52.[28] Jamali M, Lakshmanan L. HeteroMF: Recommendation in heterogeneous information networks using context dependent factor models. In Proc. the 22nd International World Wide Web Conference, May 2013, pp.643–654.[29] Ma H, King I, Lyu M R. Learning to recommend with social trust ensemble. In Proc. the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2009, pp.203–210.[30] Stern D H, Herbrich R, Graepel T. Matchbox: Large scale online Bayesian recommendations. In Proc. the 18th International World Wide Web Conference, Apr. 2009, pp.111– 120.[31] Zhang Y, Nie J. Probabilistic latent relational model for integrating heterogeneous information for recommendation. Technical Report, School of Engineering, University of California Santa Cruz, 2010.[32] Shan H, Banerjee A. Generalized probabilistic matrix factorizations for collaborative filtering. In Proc. the 10th IEEE International Conference on Data Mining, Dec. 2010, pp.1025–1030.[33] Ma H, Zhou D, Liu C, Lyu M R, King I. Recommender systems with social regularization. In Proc. the 4th International Conference on Web Search and Data Mining, Feb. 2011, pp.287–296.[34] Singh A P, Gordon G J. Relational learning via collective matrix factorization. In Proc. the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2008, pp.650–658.[35] Zhang X, Cheng J, Yuan T, Niu B, Lu H. TopRec: Domainspecific recommendation through community topic mining in social network. In Proc. the 22nd International Conference on World Wide Web, May 2013, pp.1501–1510.[36] Tang J, Wu S, Sun J, Su H. Cross-domain collaboration recommendation. In Proc. the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2012, pp.1285–1293.[37] Blei D M, McAuliffe J D. Supervised topic models. In Proc. the 21st Annual Conference on Neural Information Processing Systems, Dec. 2007, pp.121–128.[38] Blei DM, Ng A Y, Jordan M I. Latent dirichlet allocation. Journal of Machine Learning Research, 2003, 3: 993–1022.[39] Bertsekas D. Nonlinear Programming (2nd edition). Athena Scientific, 1999.[40] Liu J, Zhang F, Song X, Song Y I, Lin C Y, Hon H W. What's in a name?: An unsupervised approach to link users across communities. In Proc. the 6th ACM International Conference on Web Search and Data Mining, Feb. 2013, pp.495–504.[41] Yuan N J, Zhang F, Lian D, Zheng K, Yu S, Xie X.We know how you live: Exploring the spectrum of urban lifestyles. In Proc. the 1st Conference on Online Social Networks, Oct. 2013, pp.3–14.[42] Ma H, King I, Lyu M R. Learning to recommend with explicit and implicit social relations. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): Article No. 29.[43] Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks. In Proc. the 4th ACM Conference on Recommender Systems, Sept. 2010, pp.135–142. |