협력 필터링을 위한 자카드 계수의 성능 개선
Improving Performance of Jaccard Coefficient for Collaborative Filtering
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초록
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.
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.
참고문헌 (13)
- D. Jannach, "Accuracy Improvements for Multi-criteria Recommender Systems", Proc. of the ACM Conf. Electronic Commerce, pp.674~689 (2012)
- X. Su, "A Survey of Collaborative Filtering Techniques", Advances in Artificial Intelligence (2009)
- A. Bellogin, "Understanding Similarity Metrics in Neighbour-based Recommender Systems", Proceedings of the 2013Conference on the Theory of Information Retrieval (2013)
- H. Ahn, "A New Similarity Measure for Collaborative Filtering to Alleviate the New User Cold-starting Problem", Information Sciences, 178(1), pp.37~51 (2008)
- 이수정, "협력필터링의 데이터 희소성 해결을 위한 자카드 지수 반영의 유사도 성능 분석", 컴퓨터교육학회 논문지, 19(4), pp.59~66 (2016), 한국컴퓨터교육학회
- H. Liu, "A New User Similarity Model to Improve the Accuracy of Collaborative Filtering", Knowledge-Based Systems, 56, pp.156~166 (2014)
- J. Bobadilla, "A Collaborative Filtering Approach to Mitigate the New User Cold Start Problem", Knowledge-Based Systems, 26, pp.225~238 (2012)
- G. Koutrica, "FlexRecs : Expressing and Combining Flexible Recommendations", Proc. of the ACM SIGMOD Int'l Conf. on Management of Data, pp.745~758 (2009)
- J. Bobadilla, "A New Collaborative Filtering Metric That Improves the Behavior of Recommender Systems", Knowledge-Based Systems, 23, pp.520~528 (2010)
- K. G. Saranya, "Performance Comparison of Different Similarity Measures for Collaborative Filtering Technique", Indian Journal of Science and Technology, 9(29) (2016)
- H-F. Sun, "JacUOD : A New Similarity Measurement for Collaborative Filtering", Journal of Computer Science and Technology, 27(6), pp.1252~1260 (2012)
- B. Jeong, "Improving Memory-based Collaborative Filtering via Similarity Updating and Prediction Modulation", Information Sciences, 180(5), pp.602~612 (2010)
- M. Gao, "Userrank for Item-based Collaborative Filtering Recommendation", Information Processing Letters, 111, pp.440~446 (2011)