Applying Different Similarity Measures based on Jaccard Index in Collaborative Filtering (협력필터링에서 자카드 계수에 기반한 서로 다른 유사도 척도의 적용)

Soojung Lee (이수정)

doi:10.9708/jksci.2021.26.05.047

Applying Different Similarity Measures based on Jaccard Index in Collaborative Filtering

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(5), pp.47-53
DOI : 10.9708/jksci.2021.26.05.047
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : April 21, 2021
Accepted : May 19, 2021
Published : May 31, 2021

Soojung Lee ¹

¹경인교육대학교

Accredited

ABSTRACT

Sparse ratings data hinder reliable similarity computation between users, which degrades the performance of memory-based collaborative filtering techniques for recommender systems. Many works in the literature have been developed for solving this data sparsity problem, where the most simple and representative ones are the methods of utilizing Jaccard index. This index reflects the number of commonly rated items between two users and is mostly integrated into traditional similarity measures to compute similarity more accurately between the users. However, such integration is very straightforward with no consideration of the degree of data sparsity. This study suggests a novel idea of applying different similarity measures depending on the numeric value of Jaccard index between two users. Performance experiments are conducted to obtain optimal values of the parameters used by the proposed method and evaluate it in comparison with other relevant methods. As a result, the proposed demonstrates the best and comparable performance in prediction and recommendation accuracies.

KEYWORDS

Collaborative Filtering, Recommender System, Similarity Measure, Data Sparsity, Jaccard index

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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

Applying Different Similarity Measures based on Jaccard Index in Collaborative Filtering

ABSTRACT

KEYWORDS

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

Applying Different Similarity Measures based on Jaccard Index in Collaborative Filtering

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KEYWORDS

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