Abstract:The rating matrix is sparse, and the traditional user-based collaborative filtering cannot provide high-precision satisfactory recommendations for target users. Based on this situation, a hybrid filling collaborative filtering (HFCF) is proposed to alleviate the problem of data sparsity. From the perspective of the item, the sparse matrix is filled according to the rating information of the similar items. And from the viewpoint of users, the neighborhood of the target users is calculated according to the filled matrix. The items with the largest number of common ratings are selected to further fill the matrix. Experiments on two real datasets indicate that the proposed algorithm effectively improves the recommendation precision and relieves the data sparsity problem without any additional information.
[1] BOBADILLA J, ORTEGA F, HERNANDO A, et al. Recommender System Survey. Knowledge-Based Systems, 2013, 46: 109-132. [2] 于 洪,李俊华.一种解决新项目冷启动问题的推荐算法.软件学报, 2015, 26(6): 1395-1408. (YU H, LI J H. Algorithm to Solve the Cold-Start Problem in New Item Recommendations. Journal of Software, 2015, 26(6): 1395-1408.) [3] ADOMAVICIUS G, TUZHILIN A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749. [4] PEREIRA A L V, HRUSCHKA E R. Simultaneous Co-clustering and Learning to Address the Cold Start Problem in Recommender Systems. Knowledge-Based Systems, 2015, 82: 11-19. [5] WANG Z Q, LIANG J Y, LI R, et al. An Approach to Cold-Start Link Prediction: Establishing Connections between Non-topological and Topological Information. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(11): 2857-2870. [6] KIM H N, JI A T, HA I, et al. Collaborative Filtering Based on Collaborative Tagging for Enhancing the Quality of Recommendation. Electronic Commerce Research and Applications, 2010, 9(1): 73-83. [7] AHMADIAN S, AFSHARCHI M, MEGHDADI M. A Novel Approach Based on Multi-view Reliability Measures to Alleviate Data Sparsity in Recommender Systems. Multimedia Tools and Applications, 2019, 78(13): 17763-17798. [8] 孙小华.协同过滤系统的稀疏性与冷启动问题研究.博士学位论文.杭州:浙江大学, 2005. (SUN X H. Research of Sparsity and Cold Start Problem in Collaborative Filtering. Ph.D. Dissertation. Hangzhou, China: Zhejiang University, 2005.) [9] 张凯涵,梁吉业,赵兴旺,等.一种基于社区专家信息的协同过滤推荐算法.计算机研究与发展, 2018, 55(5): 968-976. (ZHANG K H, LIANG J Y, ZHAO X W, et al. A Collaborative Filtering Recommendation Algorithm Based on Information of Community Experts. Journal of Computer Research and Development, 2018, 55(5): 968-976.) [10] BELLOGIN A, FERNANDEZ-TOBIAS I, CANTADOR I, et al. Neighbor Selection for Cold Users in Collaborative Filtering with Positive-Only Feedback // Proc of the Conference of the Spanish Association for Artificial Intelligence. Berlin, Germany: Springer, 2018: 3-12. [11] MA H, KING I, LYU M R. Effective Missing Data Prediction for Collaborative Filtering // Proc of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2007: 39-46. [12] GUO G B, ZHANG J, ZHU F D, et al. Factored Similarity Mo-dels with Social Trust for Top-N Item Recommendation. Knowledge-Based Systems, 2017, 122: 17-25. [13] DAKHEL A M, MALAZI H T, MAHDAVI M. A Social Reco-mmender System Using Item Asymmetric Correlation. Applied Intelligence, 2018, 48(3): 527-540. [14] CHAE D K, KANG J S, KIM S W, et al. Rating Augmentation with Generative Adversarial Networks towards Accurate Collaborative Filtering // Proc of the World Wide Web Conference. New York, USA: ACM, 2019: 2616-2622. [15] WANG Q Y, YIN H Z, WANG H, et al. Enhancing Collaborative Filtering with Generative Augmentation // Proc of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2019: 548-556. [16] SARWAR B M, KARYPIS G, KONSTAN J A, et al. Application of Dimensionality Reduction in Recommender System-A Case Study // Proc of the ACM WebKDD 2000 Web Mining for E-Commerce Workshop. New York, USA; ACM, 2000: 82-90. [17] KANT S, MAHARA T. Merging User and Item Based Collaborative Filtering to Alleviate Data Sparsity. International Journal of System Assurance Engineering and Management, 2018, 9(1): 173-179. [18] 王瑞琴,潘 俊,冯建军.基于信任计算和矩阵分解的推荐算法.模式识别与人工智能, 2018, 31(9): 786-796. (WANG R Q, PAN J, FENG J J. Recommendation Algorithm Based on Trust Computation and Matrix Factorization. Pattern Re-cognition and Artificial Intelligence, 2018, 31(9): 786-796. [19] BREESE J S, HECKERMAN D, KADIE C. Empirical Analysis of Predictive Algorithms for Collaborative Filtering // Proc of the 14th Conference on Uncertainty in Artificial Intelligence. San Francisco, USA: Morgan Kaufmann, 1998: 43-52.