A Probabilistic Rating Prediction and Explanation Inference Model for Recommender Systems

作     者：WANG Hanshi FU Qiujie LIU Lizhen SONG Wei 

作者机构：Information and Engineering College Capital Normal University 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2016年第13卷第2期

页      面：79-94页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 0839[工学-网络空间安全] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported in part by National Science Foundation of China under Grants No.61303105 and 61402304 the Humanity&Social Science general project of Ministry of Education under Grants No.14YJAZH046 the Beijing Natural Science Foundation under Grants No.4154065 the Beijing Educational Committee Science and Technology Development Planned under Grants No.KM201410028017 Academic Degree Graduate Courses group projects 

主　　题：概率预测 推荐系统 推理模型 用户数量 协同过滤 推荐算法 行为预测 预测精度 

摘      要：Collaborative Filtering(CF) is a leading approach to build recommender systems which has gained considerable development and popularity. A predominant approach to CF is rating prediction recommender algorithm, aiming to predict a user s rating for those items which were not rated yet by the user. However, with the increasing number of items and users, thedata is *** is difficult to detectlatent closely relation among the items or users for predicting the user behaviors. In this paper,we enhance the rating prediction approach leading to substantial improvement of prediction accuracy by categorizing according to the genres of movies. Then the probabilities that users are interested in the genres are computed to integrate the prediction of each genre cluster. A novel probabilistic approach based on the sentiment analysis of the user reviews is also proposed to give intuitional explanations of why an item is *** test the novel recommendation approach, a new corpus of user reviews on movies obtained from the Internet Movies Database(IMDB) has been generated. Experimental results show that the proposed framework is effective and achieves a better prediction performance.