Browse > Article

Strategies for Selecting Initial Item Lists in Collaborative Filtering Recommender Systems  

Lee, Hong-Joo (Graduate School of Management, Korea Advanced Institute of Science and Technology)
Kim, Jong-Woo (School of Business, Hanyang University)
Park, Sung-Joo (Graduate School of Management, Korea Advanced Institute of Science and Technology)
Publication Information
Management Science and Financial Engineering / v.11, no.3, 2005 , pp. 137-153 More about this Journal
Abstract
Collaborative filtering-based recommendation systems make personalized recommendations based on users' ratings on products. Recommender systems must collect sufficient rating information from users to provide relevant recommendations because less user rating information results in poorer performance of recommender systems. To learn about new users, recommendation systems must first present users with an initial item list. In this study, we designed and analyzed seven selection strategies including the popularity, favorite, clustering, genre, and entropy methods. We investigated how these strategies performed using MovieLens, a public dataset. While the favorite and popularity methods tended to produce the highest average score and greatest average number of ratings, respectively, a hybrid of both favorite and popularity methods or a hybrid of demographic, favorite, and popularity methods also performed within acceptable ranges for both rating scores and numbers of ratings.
Keywords
Recommender System; Collaborative Filtering; Initial Item List;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Breese, J. S., D. Heckerman, and C. Kadie, 'Empirical analysis of predictive algorithms for collaborative filtering,' in the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998, 43-52   DOI
2 Huang, Z., H. Chen, and D. Zeng, 'Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering,' ACM Transactions on Information Systems 22, 1 (2004), 116-142   DOI   ScienceOn
3 Sarwar, B., G. Karypis, J. Konstan, and J. Riedl, 'Item-Based Collaborative Filtering Recommendation Algorithms,' in WWW10, Hong Kong, 2001, 285-295   DOI
4 Sarwar, B. M., G. Karypis, J. A. Konstan, and J. T. Riedl, 'Application of Dimensionality Reduction in Recommender System - A Case Study,' in ACM WebKDD 2000 Web Mining for E-Commerce Workshop, 2000
5 Ungar, L. H. and D. P. Foster, 'Clustering Methods for Collaborative Filtering,' in Workshop on Recommendation Systems at the Fifteenth National Conference on Artificial Intelligence, 1998
6 Herlocker, J. L., J. A. Konstan, L. G. Terveen, and J. T. Riedl, 'Evaluating Collaborative Filtering Recommender Systems,' ACM Transactions on Information Systems 22, 1 (2004), 5-53   DOI   ScienceOn
7 Ansari, A., S. Essegaier, and R. Kohli, 'Internet recommendation systems,' Journal of Marketing Research 37, 3 (2004), 363-375   DOI   ScienceOn
8 Linden, G., B. Smith, and J. York, 'Amazon. com recommendations: Item-toitem collaborative filtering,' IEEE Internet Computing 7,3 (2003), 76-80   DOI   ScienceOn
9 Konstan, J. A., B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl, 'GroupLens: Applying collaborative filtering to usenet news,' Communication of the ACM 40, 3 (1997), 77-87   DOI   ScienceOn
10 Schein, A. I., A. Popescul, L. H. Ungar, and D. M. Pennock, 'Methods and Metrics for Cold Start Recommendations,' in the ACM SIGIR '02, Tampere, Finland, 2002, 253-260   DOI
11 Mild, A. and M. Natter, 'Collaborative filtering or regression models for Internet recommendation systems?' Journal of Targeting, Measurement and Analysis of Marketing 10, 4 (2002), 304-313   DOI   ScienceOn
12 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,' in the ACM IUI'02, San Francisco, 2002,127-134   DOI
13 Mulvenna, M. D., S. S. Anand, and A. G. Buchner, 'Personalization on the net using web mining,' Communication of the ACM 43, 8 (2000), 122-125   DOI
14 Mobasher, B., R. Cooley, and J. Srivasta, 'Automatic personalization based on web usage mining,' Communication of the ACM 43,8 (2000), 142-151   DOI
15 Sarwar, B., G. Karypis, J. Konstan, and J. Riedl, 'Analysis of Recommendation Algorithms for e-Commerce,' in EC'00, Minneapolis, 2000, 158-167   DOI
16 Schein, A. I., A. Popescul, L. H. Ungar, and D. M. Pennock. 'Generative Model for Cold-Start Recommendations,' in SIGIR Workshop on Recommender Systems, New Orleans, LA, 2001
17 Pennock, D. M., E. Horvitz, S. Lawrence, and C. L. Giles, 'Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach,' in the Sixteenth Conference on Unceratainty in Aritificial Intelligence(UAI-2000), San Francisco, 2000, 473-480
18 Kim, J. W., H. J. Lee, and S. J. Park, 'Parameter Selection of Collaborative Filtering for e-Commerce Personalized Recommendation,' in The 7th International Conference on Electronic Commerce Research (ICECR-7), Dallas, U.S.A, 2004
19 Nquyen, H. and P. Haddawy. 'The Decision-Theoretic Video Advisor,' in AAAI Workshop on Recommender Systems, 1998, 76-80