Utilizing Fuzzy Logic for Recommender Systems (추천 시스템을 위한 퍼지로직의 활용)

Soojung Lee (이수정)

doi:10.9708/jksci.2018.23.08.045

Utilizing Fuzzy Logic for Recommender Systems

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2018, 23(8), pp.45-50
DOI : 10.9708/jksci.2018.23.08.045
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : July 3, 2018
Accepted : August 9, 2018
Published : August 31, 2018

Soojung Lee ¹

¹경인교육대학교

Accredited

ABSTRACT

Many of the current successful commercial recommender systems utilize collaborative filtering techniques. This technique recommends products to the active user based on product preference history of the neighbor users. Those users with similar preferences to the active user are typically named his/her neighbors. Hence, finding neighbors is critical to performance of the system. Although much effort for developing similarity measures has been devoted in the literature, there leaves a lot to be improved, especially in the aspect of handling subjectivity or vagueness in user preference ratings. This paper addresses this problem and presents a novel similarity measure using fuzzy logic for selecting neighbors. Experimental studies are conducted to reveal that the proposed measure achieved significant performance improvement.

KEYWORDS

Collaborative Filtering, Recommender System, Similarity Measure, Fuzzy Logic

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* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Utilizing Fuzzy Logic for Recommender Systems

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Utilizing Fuzzy Logic for Recommender Systems

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.