Development of a Personalized Similarity Measure using Genetic Algorithms for Collaborative Filtering (협력필터링을 위한 유전자 알고리즘을 이용한 개인화된 유사도척도 개발)

Soojung Lee (이수정)

doi:10.9708/jksci.2018.23.12.219

Development of a Personalized Similarity Measure using Genetic Algorithms for Collaborative Filtering

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2018, 23(12), pp.219-226
DOI : 10.9708/jksci.2018.23.12.219
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : October 15, 2018
Accepted : November 8, 2018
Published : December 31, 2018

Soojung Lee ¹

¹경인교육대학교

Accredited

ABSTRACT

Collaborative filtering has been most popular approach to recommend items in online recommender systems. However, collaborative filtering is known to suffer from data sparsity problem. As a simple way to overcome this problem in literature, Jaccard index has been adopted to combine with the existing similarity measures. We analyze performance of such combination in various data environments. We also find optimal weights of factors in the combination using a genetic algorithm to formulate a similarity measure. Furthermore, optimal weights are searched for each user independently, in order to reflect each user's different rating behavior. Performance of the resulting personalized similarity measure is examined using two datasets with different data characteristics. It presents overall superiority to previous measures in terms of recommendation and prediction qualities regardless of the characteristics of the data environment.

KEYWORDS

Collaborative Filtering, Recommender System, Similarity Measure, Genetic Algorithm, Data Sparsity Problem

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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

Development of a Personalized Similarity Measure using Genetic Algorithms for Collaborative Filtering

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* 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

Development of a Personalized Similarity Measure using Genetic Algorithms for Collaborative Filtering

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KEYWORDS

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