Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

Applied Sciences

Quanshen Wei¹  Baojiao Wang¹  Liang Zhang¹  Lei Zhang¹  Wen-Hsien Ho² 

[1] Business School, Qingdao University, Qingdao 266071, Shandong, China;Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 807, Taiwan;

关键词: recommender systems;    collaborative filtering;    diversity;    multi-interest;    ranking function;   

DOI  :  10.3390/app10041257

来源: DOAJ

【 摘 要 】

Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.

【 授权许可】

Unknown