Improving Performance of Jaccard Coefficient for Collaborative Filtering (협력 필터링을 위한 자카드 계수의 성능 개선)

Soojung Lee (이수정)

Improving Performance of Jaccard Coefficient for Collaborative Filtering

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2016, 21(11), pp.121-126
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

Soojung Lee ¹

¹경인교육대학교

Accredited

ABSTRACT

In recommender systems based on collaborative filtering, measuring similarity is very critical for determining the range of recommenders. Data sparsity problem is fundamental in collaborative filtering systems, which is partly solved by Jaccard coefficient combined with traditional similarity measures. This study proposes a new coefficient for improving performance of Jaccard coefficient by compensating for its drawbacks. We conducted experiments using datasets of various characteristics for performance analysis. As a result of comparison between the proposed and the similarity metric of Pearson correlation widely used up to date, it is found that the two metrics yielded competitive performance on a dense dataset while the proposed showed much better performance on a sparser dataset. Also, the result of comparing the proposed with Jaccard coefficient showed that the proposed yielded far better performance as the dataset is denser. Overall, the proposed coefficient demonstrated the best prediction and recommendation performance among the experimented metrics.

KEYWORDS

Collaborative Filtering, Recommender System, Similarity Measure, Jaccard Coefficient

Citation status

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

[confproc] D. Jannach / 2012 / Accuracy Improvements for Multi-criteria Recommender Systems / Proc. of the ACM Conf. Electronic Commerce : 674~689

[journal] X. Su / 2009 / A Survey of Collaborative Filtering Techniques / Advances in Artificial Intelligence

[confproc] A. Bellogin / 2013 / Understanding Similarity Metrics in Neighbour-based Recommender Systems / Proceedings of the 2013Conference on the Theory of Information Retrieval

[journal] H. Ahn / 2008 / A New Similarity Measure for Collaborative Filtering to Alleviate the New User Cold-starting Problem / Information Sciences 178(1) : 37~51

[journal] 이수정 / 2016 / Performance Analysis of Similarity Reflecting Jaccard Index for Solving Data Sparsity in Collaborative Filtering / 컴퓨터교육학회 논문지 / 한국컴퓨터교육학회 19(4) : 59~66

[journal] H. Liu / 2014 / A New User Similarity Model to Improve the Accuracy of Collaborative Filtering / Knowledge-Based Systems 56 : 156~166

[journal] J. Bobadilla / 2012 / A Collaborative Filtering Approach to Mitigate the New User Cold Start Problem / Knowledge-Based Systems 26 : 225~238

[confproc] G. Koutrica / 2009 / FlexRecs : Expressing and Combining Flexible Recommendations / Proc. of the ACM SIGMOD Int'l Conf. on Management of Data : 745~758

[journal] J. Bobadilla / 2010 / A New Collaborative Filtering Metric That Improves the Behavior of Recommender Systems / Knowledge-Based Systems 23 : 520~528

[journal] K. G. Saranya / 2016 / Performance Comparison of Different Similarity Measures for Collaborative Filtering Technique / Indian Journal of Science and Technology 9(29)

[journal] H-F. Sun / 2012 / JacUOD : A New Similarity Measurement for Collaborative Filtering / Journal of Computer Science and Technology 27(6) : 1252~1260

[journal] B. Jeong / 2010 / Improving Memory-based Collaborative Filtering via Similarity Updating and Prediction Modulation / Information Sciences 180(5) : 602~612

[journal] M. Gao / 2011 / Userrank for Item-based Collaborative Filtering Recommendation / Information Processing Letters 111 : 440~446

KJCKorea
Journal Central

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