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Number of Ratings and Performance in Collaborative Filtering-based Product Recommendation  

Lee Hong-Joo (한국과학기술원 테크노경영대학원)
Park Sung-Joo (한국과학기술원 테크노경영대학원)
Kim Jong-Woo (한양대학교 경영학부)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.31, no.2, 2006 , pp. 27-39 More about this Journal
Abstract
The Collaborative Filtering (CF) is one of the popular techniques for personalization in e-commerce storefronts. For CF-based recommendation, every customer needs to provide subjective evaluation ratings for some products based on his/her preference. Also, if an e-commerce site recommends a new product, some customers should rate it. However, there is no in-depth investigation on the impacts on recommendation performance of two number of ratings, i.e. the number of ratings of an individual customer and the number of ratings of an item, even though these are important factors to determine performance of CF methods. In this study, using publicly available EachMovie data set, we empirically investigate the relationships between the two number of ratings and the performance of CF. For the purpose, three analyses were executed. The first and second analyses were performed to investigate the relationship between the number of ratings of a particular customer and the recommendation performance of CF. In the third analysis, we investigate the relationship between the number of ratings on a particular item and the recommendation performance of CF. From these experiments, we can find that there are thresholds in terms of the number of ratings below which the recommendation performances increase monotonically. That is, the number of ratings of a customer and the number of ratings on an item are critical to the recommendation performance of CF when the number of ratings is less than the thresholds, but the value of the ratings decreases after the numbers of ratings pass the thresholds. The results of the experiments provide insight to making operational decisions concerning collaborative filtering in practice.
Keywords
Collaborative Filtering; Product Recommendation; Personalization; e-Commerce;
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  • Reference
1 Autonomy, http://www.autonomy.com, 2004
2 Gustos, http://www.gustos.com, 2004
3 Roh, T.H., K.J. Oh, and I. Han, 'The Collaborative Filtering Recommendation based on SOM Cluster-indexing CBR,' Expert Systems with Applications, Vol.25(2003), pp.413-423
4 Sarwar, B., G. Karypis, J. Konstan, and J. Riedl, 'Item-Based Collaborative Filtering Recommendation Algorithms,' Proceedings of WWW10,Hong Kong, (2001), pp.285-295
5 Schein, A.I., A. Popescul, L.H. Ungar, and D.M. Pennock, 'Methods and Metrics for Cold-Start Recommendations,' Proceedings of the ACM SIGIR'02, Tampere, Finland, (2002), pp.253- 260
6 Mulvenna, M.D., S.S. Anand, and A.G. Buchner, 'Personalization on the Net using Web Mining,' Communication of the ACM, Vol.43, No. 8(2000), pp.122-125   DOI
7 Kim, J.K., Y.H. Cho, W.J. Kim, J.R. Kim, J.R., and J.H. Suh, 'A Personalized Recommendation Procedure for Internet Shopping Support,' Electronic Commerce Research and Applications, Vol.1(2002), pp.301-313   DOI   ScienceOn
8 Kim, J.W., H.J. Lee, and S.J. Bae, 'Data Sparsity and Collaborative Filtering-based E-Commerce Personalized Recommendation,' Working Paper, Hanyang University, 2004
9 Gonzalez-Caro, C.N., M.L. Calderon-Benavides, J.J. Perez-Alcazar, J.C. Garcia-Diaz, and J. Delgado, 'Towards a More Comprehensive Comarison of Collaborative Filtering Algorithms,' In A.H.F. Laedner and A. L. Oliveira (eds.), SPIRE 2002, Lecture Notes in Computer Science 2476, (2002), pp.248-253
10 Linden, G., B. Smith, and J. York, 'Amazon.com Recommendations：Item-to-item Collaborative Filtering,' IEEE Internet Computing, Vol.7, No.3(2003), pp.76-80   DOI   ScienceOn
11 Rashid, A.M., I. Albert, D. Cosley, S.K. Lam, S.M. McNee, J.A. Konstan, and J. Riedl, 'Getting to Know You：Learning New User Preferences in Recommender Systems,' Proceedings of the ACM IUI'02, San Francisco, (2002), pp.127-134
12 Kim, J.W., B.H. Lee, M.J. Shaw, H. Chang, and M. Nelson, 'Application of Decision Tree Induction Techniques to Personalized Advertisements on Internet Storefront,' International Journal of Electronic Commerce, Vol.5, No.3 (2001), pp.45-62   DOI
13 김재경, 서지혜, 안도현, 조윤호, '협업 필터링 기법을 활용한 개인화된 상품 추천방법론 개발에 관한 연구', 한국지능정보시스템학회논문지, 제8권, 제2호(2002), pp.139-157
14 Shardanand, U. and P. Maes, 'Social Information Filtering：Algorithms for Automating 'Word of Mouth',' Proceedings of Conference on Human Factors in Computer Systems, (1995), pp.210-217
15 Chen, Y. and E. George, 'A Bayesian model for Collaborative Filtering,' Technical Report, Statistics Department, University of Texas at Austin, 2000
16 Net Perception, http://www.netperceptions.com, 2004
17 Ansari, A., S. Essegaier, and R. Kohli, 'Internet Recommendation Systems,' Journal of Marketing Research, Vol.37, No.3(2004), pp.363-375   DOI   ScienceOn
18 Malone, T.W., K.R. Grant, F.A. Turbak, S.A. Brobst, and M.D. Cohen, 'Intelligent Information Sharing Systems,' Communications of the ACM, Vol.30, No.5(1987), pp.390-402   DOI   ScienceOn
19 Sarwar, B., G. Karypis, J. Konstan, and J. Riedl, 'Analysis of Recommendation Algorithms for E-Commerce,' Proceedings of EC'00, Minneapolis, (2000), pp.158-167
20 Breese, J.S., D. Heckerman, and C. Kadie, 'Empirical Analysis of Predictive Algorithms for Collaborative Filtering,' Technical Report, MSR-TR-98-12, Microsoft Research, 1998
21 Mild, A. and M. Natter, 'Collaborative Filtering or Regression Models for Internet Recommendation Systems?,' Journal of Targeting, Measurement and Analysis of Marketing, Vol.10, No.4(2002), pp. 304-313   DOI
22 Mobasher, B., R. Cooley, and J. Srivastava, 'Automatic Personalization based on web usage Mining,' Communication of the ACM, Vol.43, No.8(2000), pp.142-151   DOI
23 Lee, C.H., Y.H. Kim, and P.K. Rhee, 'Web Personalization Expert with Combining Collaborative Filtering and Association Rule Mining Techniques,' Expert Systems with Applications, Vol.21, No.3(2001), pp.131-137   DOI   ScienceOn
24 김종우, 이경미, '인터넷 상점에서 개인화 광 고를 위한 장바구니 분석 기법의 활용', 경영 과학, 제17권, 제3호(2000), pp.19-30
25 BeFree, http://www.befree.com, 2004
26 Resnick, P., N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, 'GroupLens：An Open Architecture for Collaborative Filtering of Netnews,' Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work, New York, (1994), pp. 175-186
27 Cho, Y.H., J.K. Kim, and S.H. Kim, 'A Personalized Recommender System based on Web usage Mining and Decision Tree Induction,' Expert Systems with Applications, Vol.23, No.3(2002), pp.329-242   DOI   ScienceOn
28 Konstan, J.A, B.N. Miller, D. Maltz, J.L. Herlocker, L.R. Gordon, and J. Riedl, 'Group- Lens：Applying Collaborative Filtering to Usenet News,' Communication of the ACM, Vol.40, No.3(1997), pp.77-87   DOI   ScienceOn
29 Gupta, O., M. Digiovanni, H. Norita, and K. Goldberg, 'Jester 2.0：Evaluation of a new Linear time Collaborative Filtering Algorithm,' 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, (1999), pp.291-292