Abstract：For the difference of transmutative Singular Value Decomposition (SVD++) algorithm's rating rule in two stages of model training and prediction, and the same probability of List-wise Matrix Factorization (ListRank-MF) algorithm's Top-1 ranking probability caused by a large number of same rating items, an algorithm of list-wise matrix factorization combining with item popularity was proposed. Firstly, the current item to be rated was removed from the set of items that the user had used in the rating rule. Secondly, the item popularity was used to improve the Top-1 ranking probability. Then the stochastic gradient descent algorithm was used to solve the objective function and make Top-N recommendation. Based on the modified SVD++ rating rule, the proposed algorithm and the SVD++ algorithms whose objective functions are point-wise and list-wise were compared on MovieLens and Netflix datasets. Compared with the list-wise SVD++ algorithm, the value of Normalized Discounted Cumulative Gain (NDCG) of Top-N recommendation accuracy was increased by 5%-8% on MovieLens datasets and about 1% on Netflix datasets. The experimental results show that the proposed algorithm can effectively improve the Top-N recommendation accuracy.
[1] 王国霞,刘贺平.个性化推荐系统综述[J].计算机工程与应用,2012,48(7):66-76.(WANG G X, LIU H P. Survey of personalized recommendation system[J]. Computer Engineering and Applications, 2012, 48(7):66-76.) [2] DESHPANDE M, KARYPIS G. Item-based Top-N recommendation algorithms[J]. ACM Transactions on Information Systems, 2004, 22(1):143-177. [3] 项亮.推荐系统实践[M].北京:人民邮电出版社,2012:35-41.(XIANG L. Recommended System in Action[M]. Beijing:Posts & Telecom Press, 2012:35-41.) [4] SEDHAIN S, MENON A K, SANNER S, et al. AutoRec:autoencoders meet collaborative filtering[C]//Proceedings of the 2015 International Conference on World Wide Web. New York:ACM, 2015:111-112. [5] WU Y, DUBOIS C, ZHENG A X, et al. Collaborative denoising auto-encoders for top-N recommender systems[C]//Proceedings of the 2016 ACM International Conference on Web Search and Data Mining. New York:ACM, 2016:153-162. [6] HE X, LIAO L, ZHANG H, et al. Neural collaborative filtering[C]//Proceedings of the 2017 International Conference on World Wide Web. New York:ACM, 2017:173-182. [7] BELL R, KOREN Y. Lessons from the Netflix prize challenge[J]. ACM SIGKDD Explorations Newsletter, 2007, 9(2):75-79. [8] KOREN Y. Factorization meets the neighborhood:a multifaceted collaborative filtering model[C]//Proceedings of the 2008 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2008:426-434. [9] KABBUR S, NING X, KARYPIS G. FISM:factored item similarity models for top-N recommender systems[C]//Proceedings of the 2013 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2013:659-667. [10] LIU N N, YANG Q. EigenRank:a ranking-oriented approach to collaborative filtering[C]//Proceedings of the 2008 International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM, 2008:83-90. [11] WESTON J, YEE H, WEISS R J. Learning to rank recommendations with the k-order statistic loss[C]//Proceedings of the 2013 ACM Conference on Recommender Systems. New York:ACM, 2013:245-248. [12] 黄震华,张佳雯,田春岐,等.基于排序学习的推荐算法研究综述[J].软件学报,2016,27(3):691-713.(HUANG Z H, ZHANG J W, TIAN C Q, et al. Survey on learning-to-rank based recommendation algorithms[J]. Journal of Software, 2016, 27(3):691-713.) [13] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR:Bayesian personalized ranking from implicit feedback[C]//Proceedings of the 2009 Conference on Uncertainty in Artificial Intelligence. Oregon:AUAI Press, 2009:452-461. [14] LIU J, WU C, XIONG Y, et al. List-wise probabilistic matrix factorization for recommendation[J]. Information Sciences, 2014, 278:434-447. [15] WEIMER M, KARATZOGLOU A, LE Q V, et al. COFI RANK-maximum margin matrix factorization for collaborative ranking[C]//Proceedings of the 2007 International Conference on Neural Information Processing Systems. Berlin:Springer, 2007:1593-1600. [16] CAO Z, QIN T, LIU T Y, et al. Learning to rank:from pairwise approach to listwise approach[C]//Proceedings of the 2007 International Conference on Machine Learning. New York:ACM, 2007:129-136. [17] SHI Y, LARSON M, HANJALIC A. List-wise learning to rank with matrix factorization for collaborative filtering[C]//Proceedings of the 2010 ACM Conference on Recommender Systems. New York:ACM, 2010:269-272. [18] 赵向宇.Top-N协同过滤推荐技术研究[D].北京:北京理工大学,2014:21-33.(ZHAO X Y. Research on Top-N recommendation with collaborative filtering[D]. Beijing:Beijing Institute of Technology, 2014:21-33.) ZHOU Ruihuan, born in 1993, M. S. candidate. His research interests include personalized recommendation algorithm, machine learning.ZHAO Hongyu, born in 1971, Ph. D., associate professor. His research interests include pattern recognition, artificial intelligence, information theory and coding.
2018, Vol. 38 
