Hybrid Recommendation for Sparse Rating Matrix:A Heterogeneous Information Network Approach

摘要：

　　Exploiting additional item meta-data is proposed in this paper for solving data sparsity and cold start problems found in item-based collaborative filtering(CF)techniques,which are employed in recommendation systems.Additional item meta-data provides the foundation for generating a heterogeneous information network(HIN).The proposed approach is to enrich the item-based CF with diverse types of relationships existing between items in the HIN,to overcome the sparsity issue from implicit user feedback.Bayesian personalized ranking optimization technique is used for estimation and its performance is evaluated by comparing the results with the traditional item-based CF.The experimental tests prove that the proposed approach achieves better accuracy.

关键词： collaborative filtering recommendation system heterogeneous information network meta-path Bayesian personalized ranking

作者: Haiyang Zhang Ivan Ganchev Nikola S.Nikolov Zhanlin Ji Máirtín ODroma

作者单位: Telecommunications Research Centre(TRC),University of Limerick,Ireland Telecommunications Research Centre(TRC),University of Limerick,Ireland;Department of Computer System Telecommunications Research Centre(TRC),University of Limerick,Ireland;Department of Computer Scienc

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 740-744

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）