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공공누리This item is licensed Korea Open Government License

Title
Optimal Diversity of Recommendation List for Recommender Systems based on the Users’ Desire Diversity
Author(s)
Mehrjoo, SaeedMehrjoo, MehrdadHajipour, Farahnaz
Publisher
Korea Institute of Science and Technology Information
Publication Year
2019-09-30
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.
Keyword
Accuracy; Collaborative Filtering; Personal Diversity; Recommender System
Journal Title
Journal of Information Science Theory and Practice;Volume 7 Issue 3
ISSN
2287-4577
DOI
10.1633/JISTaP.7.3.3
Files in This Item:
Thumbnail Mehrjoo_V7(3)_31-39.pdf537.8 kBDownload
Appears in Collections:
8. KISTI 간행물 > JISTaP > Vol. 7 - No. 3
Type
Serial
URI
https://repository.kisti.re.kr/handle/10580/15285
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