2Labrinidis A, Jagadish H V. Challenges and opportuni- ties with big data[ J]. Proceedings of the VLDB Endow- ment, 2012, 5(12): 2032-2033.
3Ye Tao, Bickson D, Yan Qiang. Second workshop on large-scale recommender systems: research and best prac- tice [ C ] //J 8'h ACM Conference on Recommender Sys- tems, 2014 ACM. Silicon Valley: ACM Press, 2014: 385 -386.
4Hong Jongyi, Suh E H, Kim J, et al. Contextaware sys- tem for proactive personalized service based on context history [J]. Expert Systems with Applications, 2009, 36 (4) : 7448-7457.
5Pessemier T D, Deryckere T, Martens L. Extending the Bayesian classifier to a context-aware recommender system for mobile devices [ C ]//Internet and Web Applications and Services (ICIW), 2010 Fifth International Confer- ence on IEEE. Barcelona, Spain: IEEE Press, 2010: 242-247.
6Shahabi C, Chen Yishin. An adaptive recommendation system without explicit acquisition of user relevanee feed- back [J]. Distributed and Parallel Databases, 2003, 14 (2) : 173-192.
7Yang Diyi, Chen Tianqi, Zhang Weinan, et al. Local implicit feedback mining for music recommendation [ C 1// the 6th ACM Conference on Recommender Systems, 2012 ACM. Dublin: ACM Press, 2012: 91-98.
8Rafailidis D, Nanopoulos A. Modeling the dynamics of user preferences in coupled tensor factorization [ C ] // the 8'h ACM Conference on Recommender Systems, 2014 ACM. Silicon Valley: ACM Press, 2014: 321- 324.
9Oh K J, Lee W J, Lim C G, et al. Personalized news recommendation using classified keywords to capture user preference [ C ] //16th Advanced Communication Technology (ICACT) , 2014 International Conference on IEEE. South Korea: IEEE Press, 2014: 1283-1287.
10Taktics G, Piltiszy I, N6meth B, et al. Scalable collabo- rative filtering approaches for large recommender systems [ J]. The Journal of Machine Learning Research, 2009, 10(12) : 623-656.
