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http://dx.doi.org/10.1633/JISTaP.2019.7.3.3

Optimal Diversity of Recommendation List for Recommender Systems based on the Users' Desire Diversity  

Mehrjoo, Saeed (Department of Computer Science and Engineering, Dariun Branch, Islamic Azad University)
Mehrjoo, Mehrdad (Department of Electrical and Computer Engineering, University of Manitoba)
Hajipour, Farahnaz (Biomedical Engineering Program, University of Manitoba)
Publication Information
Journal of Information Science Theory and Practice / v.7, no.3, 2019 , pp. 31-39 More about this Journal
Abstract
Nowadays, recommender systems suggest lists of items to users considering not only accuracy but also diversity and novelty. However, suggesting the most diverse list of items to all users is not always acceptable, since different users prefer and/or tolerate different degree of diversity. Hence suggesting a personalized list with a diversity degree considering each user preference would improve the efficiency of recommender systems. The main contribution and novelty of this study is to tune the diversity degree of the recommendation list based on the users' variety-seeking feature, which ultimately leads to users' satisfaction. The proposed approach considers the similarity of users' desire diversity as a new parameter in addition to the usual similarity of users in the state-of-the-art collaborative filtering algorithm. Experimental results show that the proposed approach improves the personal diversity criterion comparing to the closest method in the literature, without decreasing accuracy.
Keywords
accuracy; collaborative filtering; personal diversity; recommender system;
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