Leveraging the Dynamic Changes from Items to Improve Recommendation

摘要：

　　User-generated reviews contain rich information,which has been ignored by most of recommender systems.Recently,some recommender systems using reviews with deep learning techniques have demonstrated that they can potentially alleviate the sparsity problem and improve the quality of recommendation.However,they only consider the dynamic interests from users but ignoring the changed properties of items.In this paper,we present a deep model which can capture not only the common users behaviors,the changed users interests and fundamental item properties,but also the changed properties of items.Experimental results conducted on a variety of datasets demonstrate that our model significantly outperforms all baseline recommender systems.

关键词： Recommender system Dynamic item reviews Deep learning

作者: Zongze Jin Yun Zhang Weimin Mu Weiping Wang Hai Jin

作者单位: School of Cyber Security,University of Chinese Academy of Sciences,Beijing,China;Institute of Inform Institute of Information Engineering,Chinese Academy of Sciences,Beijing,China;National Engineering Institute of Information Engineering,Chinese Academy of Sciences,Beijing,China;School of Computer Sc

会议类型: 国际会议

会议名称: The 37th International Conference on Conceptual Modeling(第37届概念建模国际会议(ER2018)

会议地点: 西安

会议语种:英文

页码: 507-519

在线出版日期: 2018-10-22（万方平台首次上网日期，不代表论文的发表时间）