参考文献(References):
[1] SHAO L, ZHANG J, Wei Y, et al. Personalized QoS prediction for Web services via collaborative filtering [C] ∥ 2007 IEEE International Conference on Web Services. Salt
Lake City: IEEE, 2007: 439-446.
[2] ZHENG Z, MA H, LYU M R, et al. QoS-aware Web service recommendation by collaborative filtering [J]. IEEE Transactions on Services Computing, 2011, 4(2): 140-152.
[3] KONSTAN J A, MILLER B N, MALTZ D, et al. GroupLens: applying collaborative filtering to Usenet news [J]. Communications of the ACM, 1997, 40(3): 77-87.
[4] RESNICK P, IACOVOU N, SUCHAK M, et al. GroupLens: an open architecture for collaborative filtering of netnews [C] ∥ Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work. New York: ACM, 1994: 175-186.
[5] LINDEN G, SMITH B, YORK J. Amazon. com recommendations: item-to-item collaborative filtering [J]. IEEE Internet computing, 2003, 7(1): 76-80.
[6] HILL W, STEAD L, ROSENSTEIN M, et al. Recommending and evaluating choices in a virtual community of use [C] ∥ Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM, 1995: 194-201.
[7] DESHPANDE M, KARYPIS G. Item-based top-n recommendation algorithms [J]. ACM Transactions on Information Systems (TOIS), 2004, 22(1): 143-177.
[8] BALABANOVI M, SHOHAM Y. Fab: content-based, collaborative recommendation [J].Communications of the ACM, 1997, 40(3): 66-72.
[9] STOJANOVA D, CECI M, APPICE A, et al. Network regression with predictive clustering trees [J]. Data Mining and Knowledge Discovery, 2012, 25(2): 378-413.
[10] MILLER B N, ALBERT I, LAM S K, et al. MovieLens unplugged: experiences with an occasionally connected recommender system [C] ∥ Proceedings of the 8th International
Conference on Intelligent User Interfaces. Florida: ACM, 2003: 263-266.
[11] SU X, KHOSHGOFTAAR T M. A survey of collaborative filtering techniques [J].Advances in Artificial Intelligence, 2009, 2009(4): 1-19.
[12] ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions [J]. IEEE Transactions on
Knowledge and Data Engineering, 2005, 17(6): 734-749.
[13] BREESE J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering [C] ∥ Proceedings of the Fourteenth Conference on Uncertainty
in Artificial Intelligence. Madison: Morgan Kaufmann Publishers Inc, 1998: 43-52.
[14] CARDELLINI V, CASALICCHIO E, GRASSI V, et al. Flowbased service selection for Web service composition supporting multiple QoS classes[C]∥2007 IEEE International Conference on Web Services. Salt Lake City: IEEE, 2007: 743-750.
[15] JIA Z, YANG Y, GAO W, et al. User-based collaborative filtering for tourist attraction recommendations\[C\]∥Computational Intelligence and Communication Technology
(CICT). Paris: IEEE, 2015: 22-25.
[16] SARWAR B, KARYPIS G, KONSTAN J, et al.Item-based collaborative filtering recommendation algorithms [C]∥Proceedings of the 10th International Conference on World
Wide Web. Hong Kong: ACM, 2001: 285-295.
[17] ZHENG Z, MA H, LYU M R, et al. Wsrec: a collaborative filtering based web service recommender system [C] ∥ 2009 IEEE International Conference on Web Services. Los
Angeles: IEEE, 2009: 437-444.
[18] SHAO L, ZHANG J, WEI Y, et al. Personalized QoS prediction for Web services via collaborative filtering [C] ∥ 2007 IEEE International Conference on Web Services. Salt
Lake City: IEEE, 2007: 439-446.
[19] ZHENG Z, ZHANG Y, LYU M R. Distributed QoS evaluation for real-world Web services [C] ∥ 2010 IEEE International Conference on Web Services. Miami: IEEE, 2010: 83-90.
[20] RENNIE J D M, SREBRO N. Fast maximum margin matrix factorization for collaborative prediction [C] ∥Proceedings of the 22nd International Conference on Machine Learning. Bonn: ACM, 2005: 713-719.
[21] SALAKHUTDINOV R, MNIH A. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo [C] ∥ Proceedings of the 25th International Conference on Machine Learning. Helsinki: ACM, 2008: 880-88.
[22] SALAKHUTDINOV R, MNIH A. Probabilistic matrix factorization [J]. Advances in Neural Information Processing Systems, 2007, 49(8): 1257-1264.
[23] LO W, YIN J, DENG S, et al. Collaborative Web service QoS prediction with location-based regularization [C] ∥ 2012 IEEE 19th International Conference on Web Services.
Honolulu: IEEE, 2012: 464-471.
[24] HE P, ZHU J, ZHENG Z, et al. Location-based hierarchical matrix factorization for Web service recommendation [C] ∥ 2014 IEEE International Conference on Web Services. Anchorage: IEEE, 2014: 297-304.
[25] XU Y, YIN J, LO W, et al. Personalized locationaware QoS prediction for Web services using probabilistic matrix factorization [C] ∥ 14th International Conference
on Web Information Systems Engineering. Nanjing: WISE, 2013: 229-242.
[26] CHEN X, ZHENG Z, LIU X, et al. Personalized QoS-aware web service recommendation and visualization [J]. IEEE Transactions on Services Computing, 2013, 6(1): 35-47.
[27] CHEN X, LIU X, HUANG Z, et al. Regionknn: a scalable hybrid collaborative filtering algorithm for personalized Web service recommendation [C] ∥ IEEE International
Conference on Web Services. Miami: IEEE, 2010: 9-16.
[28] KOREN Y. Collaborative filtering with temporaldynamics [J]. Communications of the ACM, 2010,53(4): 89-97. |