[1] DURAO F, DOLOG P. Improving tag-based recommendation with the collaborative value of wiki pages for knowledge sharing[J]. Journal of Ambient Intelligence Humanized Computing, 2014, 5(1):21-38. [2] LINDEN G, SMITH B, YORK J. Amazon.com recommendation:item-to-item collaborative filtering[J]. IEEE Internet Computing, 2003, 7(1):76-80. [3] ELMONGUI H G, MANSOUR R, MORSY H, et al. TRUPI:Twitter recommendation based on user' personal interests[C]//International Conference on Intelligent Text Processing and Computational Linguistics, LNCS 9042. Berlin:Springer, 2015:272-284. [4] DAVIDSON J, LIEBALD B, LIU J, et al. The YouTube video recommendation system[C]//Proceeding of the 4th ACM Conference on Recommender Systems. New York:ACM, 2010:293-296. [5] SHI Y, LARSON M, HANJALIC A. Collaborative filtering beyond the user-item matrix:a survey of the state of the art and future challenges[J]. ACM Computing Surveys, 2014, 47(1):Article No. 3. [6] 张付志,刘赛,李忠华,等.融合用户评论和环境信息的协同过滤推荐算法[J].小型微型计算机系统,2014, 35(2):228-232.(ZHANG F Z, LIU S, LI Z H, et al. Collaborative filtering recommendation algorithm incorporating user's reviews and contextual information[J]. Journal of Chinese Computer Systems,2014, 35(2):228-232.) [7] WANG B, HUANG J, OU L, et al. A collaborative filtering algorithm fusing user-based, item-based and social networks[C]//Proceedings of the 2015 IEEE International Conference on Big Data. Washington, DC:IEEE Computer Society, 2015:2337-2343. [8] JABAKJI A, DAG H. Improving item-based recommendation accuracy with user's preferences on Apache Mahout[C]//Proceedings of the 2016 IEEE Internetional Conference on Big Data. Piscataway, NJ:IEEE, 2017:1742-1749. [9] LI S. Collaborative filtering recommendation algorithm based on cloud model clustering of multi-indicators item evaluation[C]//Proceedings of the 2011 International Conference on Business Computing and Global Informatization. Washington, DC:IEEE Computer Society, 2011:645-648. [10] KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8):30-37. [11] 涂丹丹,舒承椿,余海燕.基于联合概率矩阵分解的上下文广告推荐算法[J]. 软件学报,2013,24(3):454-464.(TU D D, SHU C C, YU H Y. Using unified probabilistic matrix factorization for contextual advertisement recommendation[J]. Journal of Software, 2013, 24(3):454-464.) [12] 郭磊,马军,陈竹敏,等.一种结合推荐对象间关联关系的社会化推荐算法[J]. 计算机学报, 2014, 37(1):219-228.(GUO L, MA J, CHEN Z M, et al. Incorporating item relations for social recommendation[J]. Chinese Journal of Computers, 2014, 37(1):219-228.) [13] YANG J, MCAULEY J, LESKOVEC J. Community detection in networks with node attributes[C]//Proceedings of the 2013 IEEE 13th International Conference on Data Mining. Washington, DC:IEEE Computer Society, 2013:1151-1156. [14] TANG J, GAO H, HU X, et al. Exploiting homophily effect for trust prediction[C]//Proceedings of the 6th ACM International Conference on Web Search and Data Mining. New York:ACM, 2013:53-62. [15] 孟祥武,刘树栋,张玉洁,等.社会化推荐系统研究[J].软件学报, 2015,26(6):1356-1372.(MENG X W, LIU S D, ZHANG Y J, et al. Research on social recommender systems[J]. Journal of Software, 2015, 26(6):1356-1372.) [16] YAHYAOUI H, AL-MUTAIRI A. A feature-based trust sequence classification algorithm[J]. Information Science, 2016, 328(C):455-484. [17] FANG H, BAO Y, ZHANG J. Leveraging decomposed trust in probabilistic matrix factorization for effective recommendation[C]//Proceedings of the 28th AAAI Conference on Artificial Intelligence. Menlo Park, CA:AAAI Press, 2014:30-36. [18] MA H, YANG H, LYU M R, et al. Social recommendation using probailistic matrix factorization[C]//Proceedings of the 17th ACM Conference on Information and Knowledge Management. New York:ACM, 2008:931-940. |