[1] TSIHRINTZIS G A, VIRVOU M, JAIN L C. Multimedia services in intelligent environments: Advances in recommender systems[M]// Smart Innovation, Systems and Technologies(SIST) Book Series. Springer, 2013:24-26.
[2] PARK D H, KIM H K, CHOI I Y, et al. A literature review and classification of recommender systems research[J]. Expert Systems with Applications, 2012,39(11):10059-10072.
[3] BALABANOVIC M, SHOHAM Y. Fab: Content-based, collaborative recommendation[J]. Communications of the ACM, 1997,40(3):66-72.
[4] LATHIA N, HAILES S, CAPRA L. Temporal diversity in recommender systems[C]// Proceedings of ACM SIGIR International Conference on Research and Development in Information Retrieval. 2010:210-217.
[5] 〖JP+1〗GOLDBERG K, ROEDER T, GUPTA D, et al. Eigentaste: A constant time collaborative filtering algorithm[J]. Information Retrieval, 2001,4(2):133-151.
[6] TAN H, YE H. A collaborative filtering recommendation algorithm based on item classification[C]// Proceedings of the Pacific-Asia Conference on Circuits, Communications and Systems. 2009:694-697.
[7] KIM H N, JI A T, YEON C, et al. A user-Item predictive model for collaborative filtering recommendation[C]// Proceedings of the 11th International Conference on User Modeling. 2007:324-328.
[8] 〖JP+1〗BOBADILLA J, ORTEGA F, HERNANDO A, et al. Improving collaborative filtering recommender system results and performance using genetic algorithms[J]. Knowledge-Based Systems, 2011,24(8):1310-1316.
[9] CHAKRABORTY P S. A scalable collaborative filtering based recommender system using incremental clustering[C]// Proceedings of the IEEE International Advance Computing Conference. 2009:1526-1529.
[10]YANG H Z, LI L. An enhanced collaborative filtering algorithm based on time weight [C]// Proceedings of the International Symposium on Information Engineering and Electronic Commerce. 2009:262-265.
[11]〖JP2〗ACILAR A M, ARSLAN A. A collaborative filtering method based on artificial immune network[J]. Expert Systems with Applications, 2009,36(4):8324-8332.
[12]RAFTER R, O’MAHONY M P, HURLEY N J, et al. What have the neighbours ever done for us? A collaborative filtering perspective[C]// Proceedings of the 17th International Conference on User Modeling. 2009:355-360.
[13]WANG J, DE VRIES A P, REINDERS M J T. Unifying user-based and item-based collaborative filtering approaches by similarity fusion[C]// Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2006:501-508.
[14]PARK C H, KAHNG M S. Temporal dynamics in music listening behavior: A case study of online music service[C]// Proceedings of the IEEE/ACIS 9th International Conference on Computer and Information Science. 2010:573-578.
[15]孙光福,吴乐,刘淇,等. 基于时序行为的协同过滤推荐算法[J]. 软件学报, 2013(11):2721-2733.
[16]LIU C, CHENG J. Personalized search recommendation based on gradual forgetting collaborative filtering strategy[C]// Proceedings of the 3rd International Conference on Intelligent Information Technology Application. 2009:475-478.
[17]XIONG L, CHEN X, HUANG T K, et al. Temporal collaborative filtering with Bayesian probabilistic tensor factorization[C]// Proceedings of SIAM International Conference on Data Mining. 2010:211-222.
[18]HUANG C L, HUANG W L. Handling sequential pattern decay:Developing a two-stage collaborative recommender system[J]. Electronic Commerce Research, 2009,8(3):117-129.
[19]〖JP2〗KOREN Y. Collaborative filtering with temporal dynamics[C]// Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009:89-97.
[20]JEON H, KIM T, CHOI J. Personalized information retrievalby using adaptive user profiling and collaborative filtering[J]. Advances in Information Sciences and Service Sciences, 2010,2(4):134-142.
[21]SCHAFER J B. The application of data-mining to recommender systems[M]// Encyclopedia of Data Warehousing and Mining. 2ed. 2009:45-50.
[22]陈功平,王红. 改进Pearson相关系数的个性化推荐算法[J]. 山东农业大学学报(自然科学版), 2016,47(6):940-944.
[23]朱郁筱,吕琳媛. 推荐系统评价指标综述[J]. 电子科技大学学报, 2012,41(2):163-175.
[24]BEDI P, AGARWA S, SINGHA L A, et al. A novel semantic clustering approach for reasonable diversity in news recommendations[C]// Proceedings of International Conference on Computing Intelligence and Data Mining. 2015:437-445. |