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Open AccessArticle

Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

1
Business School, Qingdao University, Qingdao 266071, Shandong, China
2
Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 807, Taiwan
3
Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 807, Taiwan
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(4), 1257; https://doi.org/10.3390/app10041257
Received: 17 December 2019 / Revised: 22 January 2020 / Accepted: 24 January 2020 / Published: 13 February 2020
(This article belongs to the Special Issue Selected Papers from IMETI 2018)
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. View Full-Text
Keywords: recommender systems; collaborative filtering; diversity; multi-interest; ranking function recommender systems; collaborative filtering; diversity; multi-interest; ranking function
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MDPI and ACS Style

Zhang, L.; Wei, Q.; Zhang, L.; Wang, B.; Ho, W.-H. Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems. Appl. Sci. 2020, 10, 1257.

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