[1] Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749
[2] Herlocker J L, Konstan J A, Borchers A, Riedl J. An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'99. New York, NY, USA: ACM, 1999. 230-237
[3] Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW'01. New York, NY, USA: ACM, 2001. 285-295
[4] Ungar L H, Foster D P. Clustering methods for collaborative filtering. In: Proceedings of the 1998 Workshop on Recommender Systems. Menlo Park: AAAI, 1998. 114-129
[5] Getoor L, Sahami M. Using probabilistic relational models for collaborative filtering. In: Proceedings of the 1999 Workshop on Web Usage Analysis and User Profiling. San Diego: Springer-Verlag, 1999. 83-96
[6] Chien Y H, George E I. A Bayesian model for collaborative filtering. In: Proceedings of the 7th International Workshop on Artificial Intelligence and Statistics. San Francsico, CA: Morgan Kaufmann, 1999. 187-192
[7] Hofmann T. Collaborative filtering via Gaussian probabilistic latent semantic analysis. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'03. New York, NY, USA: ACM, 2003. 259-266
[8] Sarwar B M, Karypis G, Konstan J A, Riedl J. Application of dimensionality reduction in recommender system — a case study. In: Proceedings of the 2000 ACM-SIGKDD Conference on Knowledge Discovery in Databases, Web Mining For E-Commerce Workshop, WEBKDD'2000. Boston, MA, USA: ACM, 2000. 1-12
[9] Hofmann T. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 2004, 22(1): 89-115
[10] Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. IEEE Computer, 2009, 42(8): 30-37
[11] Salakhutdinov R, Mnih A. Probabilistic matrix factorization. In: Proceedings of the 2007 Conference on Advances in Neural Information Processing Systems 20. Vancouver, BC, CA: Curran Associates Inc., 2007. 1257-1264
[12] Takács G, Pilászy I, Németh B, Tikk D. Matrix factorization and neighbor based algorithms for the netflix prize problem. In: Proceedings of the 2008 ACM Conference on Recommender Systems. New York, NY, USA: Association for Computing Machinery, 2008. 267-274
[13] Zhen Y, Li W J, Yeung D Y. Tagicofi: tag informed collaborative filtering. In: Proceedings of the 3rd ACM Conference on Recommender Systems. New York, NY, USA: Association for Computing Machinery, 2009. 69-76
[14] Zhou T C, Ma H, King I, Lyu M R. Tagrec: leveraging tagging wisdom for recommendation. In: Proceedings of the 12th IEEE International Conference on Computational Science and Engineering, CSE 2009. Vancouver, BC, CA: IEEE Computer Society, 2009. 194-199
[15] Ma H, Yang H X, Lyu M R, King I. SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM'08. New York, NY, USA: ACM, 2008. 978-991
[16] Ma H, King I, Lyu M R. Learning to recommend with social trust ensemble. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'09. New York, NY, USA: ACM, 2009. 203-210
[17] Jamali M, Ester M. TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'09. New York, NY, USA: ACM, 2009. 397-406
[18] Guo L, Ma J, Chen Z M, Jiang H R. Learning to recommend with social relation ensemble. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM'12. New York, NY, USA: ACM, 2012. 2599-2602
[19] Wu L, Chen E H, Liu Q, Xu L L, Bao T F, Zhang L. Leveraging tagging for neighborhood-aware probabilistic matrix factorization. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM'12. New York, NY, USA: ACM, 2012. 1854-1858
[20] Blei D M, Ng A, Jordan M. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003, 3(1): 993-1022
[21] Zhang Y M. Research on Expert-level Network Relationships Construction Integrating Explicit and Implicit Relationships [Master dissertation], Kunming University of Science and Technology, China, 2013.
[22] Tian W, Shen T, Yu Z T, Guo J Y, Xian Y T. A Chinese expert name disambiguation approach based on spectral clustering with the expert page-associated relationships. In: Proceedings of the 2013 Chinese Intelligent Automation Conference. Yangzhou, JS, China: Springer, 2013. 245-253
[23] Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2010. 135-142
[24] Koren Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'08. New York, NY, USA: ACM, 2008. 426-434