Collaborative Recommendation Framework Based on Ratings and Textual Reviews
TAN Yunzhi, ZHANG Min, LIU Yiqun, MA Shaoping
State Key Laboratory of Intelligent Technology and Systems, Beijing 100084 Tsinghua National Laboratory for Information Science and Technology, Beijing 100084 Department of Computer Science and Technology, Tsinghua University, Beijing 100084
Abstract:The feedback of users usually contains a numeric rating and a textual review. In this paper, textual review information is used to learn the distributions of item features on different topics and the user preference to different features of items. Then, the topic-based user preference similarity is incorporated into the traditional collaborative filtering recommendation systems. A recommendation framework based on ratings and textual reviews is proposed. With the proposed framework, review information can be easily introduced into the existing recommendation algorithms. By employing textual reviews, the problem of data sparsity in the traditional recommendation algorithms is relieved. Experiments are conducted on 22 real-world datasets from Amazon and the experimental results demonstrate the advantages and the effectiveness of the proposed framework.
谭云志,张 敏,刘奕群,马少平. 基于用户评分和评论信息的协同推荐框架*[J]. 模式识别与人工智能, 2016, 29(4): 359-366.
TAN Yunzhi, ZHANG Min, LIU Yiqun, MA Shaoping. Collaborative Recommendation Framework Based on Ratings and Textual Reviews. , 2016, 29(4): 359-366.
[1] SU X Y, KHOSHGOFTAAR T M. A Survey of Collaborative Fil-tering Techniques // AGUIRRE A H, BORJA R M, GARCIA C A R, eds. Advances in Artificial Intelligence. New York, USA: Hindawi Publishing Corporation, 2009. DOI: 10.1155/2009/421425. [2] GOLDBERG K, ROEDER T, GUPTA D, et al. Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval, 2001, 4(2): 133-151. [3] MELVILLE P, MOONEY R J, NAGARAJAN R. Content-Boosted Collaborative Filtering for Improved Recommendations // Proc of the 18th National Conference on Artificial Intelligence. Edmonton, Canada, 2002: 187-192. [4] ZIEGLER C N, LAUSEN G, SCHMIDT-THIEME L. Taxonomy-Driven Computation of Product Recommendations // Proc of the 13th ACM International Conference on Information and Knowledge Management. Washington, USA, 2004: 406-415. [5] GANU G, ELHADAD N, MARIAN A. Beyond the Stars: Improving Rating Predictions Using Review Text Content[J/OL]. [2015-04-24]. http://paul.rutgers.edu/~gganu/resources/WebDB.pdf. [6] JAKOB N, WEBER S H, MLLER M C, et al. Beyond the Stars: Exploiting Free-Text User Reviews to Improve the Accuracy of Movie Recommendations // Proc of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion. Hong Kong, China, 2009: 57-64. [7] MUSAT C C, LIANG Y Z, FALTINGS B. Recommendation Using Textual Opinions // Proc of the 23rd International Joint Conference on Artificial Intelligence. Beijing, China, 2013: 2684-2690.
[8] MCAULEY J, LESKOVEC J. Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text // Proc of the 7th ACM Conference on Recommender Systems. Hong Kong, China, 2013: 165-172. [9] LEUNG C W K, CHAN S C F, CHUNG F L. Integrating Collaborative Filtering and Sentiment Analysis: A Rating Inference Approach // Proc of the ECAI Workshop on Recommender Systems. Riva del Garda, Italy, 2006: 62-66. [10] ZHANG Y F, LAI G K, ZHANG M, et al. Explicit Factor Models for Explainable Recommendation Based on Phrase-Level Sentiment Analysis // Proc of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. Gold Coast, Australia, 2014: 83-92. [11] YU X H, LIU Y, HUANG J X J, et al. Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain. IEEE Trans on Knowledge and Data Engineering, 2012, 24(4): 720-734. [12] TAN Y Z, ZHANG Y F, ZHANG M, et al. A Unified Framework for Emotional Elements Extraction Based on Finite State Matching Machine // Proc of the 2nd Conference on Natural Language Processing and Chinese Computing. Chongqing, China, 2013: 60-71. [13] BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003, 3: 993-1022. [14] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-Based Co-llaborative Filtering Recommendation Algorithms // Proc of the 10th International Conference on World Wide Web. Hong Kong, China, 2001: 285-295. [15] KOREN Y. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model // Proc of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2008: 426-434. [16] HOFMANN T. Latent Semantic Models for Collaborative Filtering. ACM Trans on Information Systems, 2004, 22(1): 89-115.