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http://dx.doi.org/10.32431/kace.2018.21.5.006

A New Similarity Measure using Fuzzy Logic for User-based Collaborative Filtering  

Lee, Soojung (경인교육대학교 컴퓨터교육과)
Publication Information
The Journal of Korean Association of Computer Education / v.21, no.5, 2018 , pp. 61-68 More about this Journal
Abstract
Collaborative filtering is a fundamental technique implemented in many commercial recommender systems and provides a successful service to online users. This technique recommends items by referring to other users who have similar rating records to the current user. Hence, similarity measures critically affect the system performance. This study addresses problems of previous similarity measures and suggests a new similarity measure. The proposed measure reflects the subjectivity or vagueness of user ratings and the users' rating behavior by using fuzzy logic. We conduct experimental studies for performance evaluation, whose results show that the proposed measure demonstrates outstanding performance improvements in terms of prediction accuracy and recommendation accuracy.
Keywords
Recommender System; Collaborative Filtering; Similarity Measure; Fuzzy Logic; User-based Collaborative Filtering;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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