Abstract
The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of items that will be of interest to a certain user. User-based collaborative filtering is the most successful technology for building recommender systems to date and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers, which in typical commercial applications can be several millions. To address these scalability concerns model-based recommendation techniques have been developed. These techniques analyze the user--item matrix to discover relations between the different items and use these relations to compute the list of recommendations.In this article, we present one such class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on eight real datasets shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.
- Aggarwal, C., Wolf, J., Wu, K., and Yu, P. 1999. Horting hatches an egg: A new graph-theoretic approach to collaborative filtering. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, New York. Google Scholar
- Agrawal, R., Imielinski, T., and Swami, A. 1993. Mining association rules between sets of items in large databases. In Proceedings of 1993 ACM-SIGMOD International Conference on Management of Data (Washington, D.C). ACM, New York. Google Scholar
- Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A. 1996. Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smith, and R. Uthurusamy, Eds. AAAI/MIT Press, Cambridge, Mass., 307--328. Google Scholar
- Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference (Santiago, Chile.). 487--499. Google Scholar
- Balabanovic, M. and Shoham, Y. 1997. FAB: Content-based collaborative recommendation. Commun. ACM 40, 3 (Mar.). Google Scholar
- Basu, C., Hirsh, H., and Cohen, W. 1998. Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the 1998 Workshop on Recommender Systems. AAAI Press, Reston, Va. 11--15. Google Scholar
- Beeferman, D. and Berger, A. 2000. Agglomerative clustering of a search engine query log. In Proceedings of ACM SIGKDD International Conference. ACM, New York, 407--415. Google Scholar
- Billsus, D. and Pazzani, M. J. 1998. Learning collaborative information filters. In Proceedings of ICML. 46--53. Google Scholar
- Breese, J., Heckerman, D., and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. 43--52. Google Scholar
- Chan, P. 1999. A non-invasive learning approach to building web user profiles. In Proceedings of ACM SIGKDD International Conference. ACM, New York.Google Scholar
- Delcher, A. L., Harmon, D., Kasif, S., White, O., and Salzberg, S. L. 1998. Improved microbial gene identification with glimmer. Nucleic Acid Res. 27, 23, 4436--4641.Google Scholar
- Demiriz, A. 2001. An association mining-based product recommender. In NFORMS Miami 2001 Annual Meeting Cluster: Data Mining.Google Scholar
- Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. 1992. Using collaborative filtering to weave an information tapestry. Commun. ACM 35, 12, 61--70. Google Scholar
- Heckerman, D., Chickering, D., Meek, C., Rounthwaite, R., and Kadie, C. 2000. Dependency networks for inference, collaborative filtering, and data visualization. J. Mach. Learn. Res. 1, 49--75. Google Scholar
- Herlocker, J., Konstan, J., Borchers, A., and Riedl, J. 1999. An algorithm framework for performing collaborative filtering. In Proceedings of SIGIR. ACM, New York, 77--87. Google Scholar
- Hill, W., Stead, L., Rosenstein, M., and Furnas, G. 1995. Recommending and evaluating choices in a virtual community of use. In Proceedings of CHI. Google Scholar
- Karypis, G. 2001. Experimental evaluation of item-based top-n recommendation algorithms. In Proceedings of the ACM Conference on Information and Knowledge Management. ACM, New York. Google Scholar
- Kitts, B., Freed, D., and Vrieze, M. 2000. Cross-sell: A fast promotion-tunable customer--item recommendation method based on conditional independent probabilities. In Proceedings of ACM SIGKDD International Conference. ACM, New York, 437--446. Google Scholar
- Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., and Riedl, J. 1997. GroupLens: Applying collaborative filtering to Usenet news. Commun. ACM 40, 3, 77--87. Google Scholar
- Lin, W., Alvarez, S., and Ruiz, C. 2000. Collaborative recommendation via adaptive association rule mining. In Proceedings of the International Workshop on Web Mining for E-Commerce (WEBKDD'2000).Google Scholar
- McJones, P. and DeTreville, J. 1997. Each to each programmer's reference manual. Tech. Rep. 1997-023, Systems Research Center. http://research.compaq.com/SRC/eachmovie/.Google Scholar
- Mobasher, B., Cooley, R., and Srivastava, J. 2000. Automatic personalization based on web usage mining. Commun. ACM 43, 8, 142--151. Google Scholar
- Mobasher, B., Dai, H., Luo, T., Nakagawa, M., and Witshire, J. 2000. Discovery of aggregate usage profiles for web personalization. In Proceedings of the WebKDD Workshop.Google Scholar
- MovieLens 2003. Available at http://www.grouplens.org/data.Google Scholar
- Resnick, P. and Varian, H. R. 1997. Recommender systems. Commun. ACM 40, 3, 56--58. Google Scholar
- Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. 1994. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of CSCW. Google Scholar
- Salton, G. 1989. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, Reading, Mass. Google Scholar
- Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. 2000. Analysis of recommendation algorithms for e-commerce. In Proceedings of ACM E-Commerce. ACM, New York. Google Scholar
- Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. 2001. Item-based collaborative filtering recommendation algorithms. In WWW10. Google Scholar
- Schafer, J., Konstan, J., and Riedl, J. 1999. Recommender systems in e-commerce. In Proceedings of ACM E-Commerce. ACM, New York. Google Scholar
- Seno, M. and Karypis, G. 2001. Lpminer: An algorithm for finding frequent itemsets using length-decreasing support constraint. In Proceedings of the IEEE International Conference on Data Mining. Also available as a UMN-CS technical report, TR# 01-026. Google Scholar
- Shardanand, U. and Maes, P. 1995. Social information filtering: Algorithms for automating "word of mouth". In Proceedings of the ACM CHI'95 Conference on Human Factors in Computing Systems. ACM, New York, 210--217. Google Scholar
- Terveen, L., Hill, W., Amento, B., McDonald, D., and Creter, J. 1997. PHOAKS: A system for sharing recommendations. Commun. ACM 40, 3, 59--62. Google Scholar
- Ungar, L. H. and Foster, D. P. 1998. Clustering methods for collaborative filtering. In Workshop on Recommendation Systems at the 15th National Conference on Artificial Intelligence.Google Scholar
Index Terms
- Item-based top-N recommendation algorithms
Recommendations
Evaluation of Item-Based Top-N Recommendation Algorithms
CIKM '01: Proceedings of the tenth international conference on Information and knowledge managementThe explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of N items that will be of interest to a certain user. ...
Generating Top-N Items Recommendation Set Using Collaborative, Content Based Filtering and Rating Variance
AbstractThe main purpose of any recommendation system is to recommend items of users’ interest. Mostly content and collaborative filtering are widely used recommendation systems. Matrix factorization technique is also used by many recommendation systems. ...
Local Item-Item Models For Top-N Recommendation
RecSys '16: Proceedings of the 10th ACM Conference on Recommender SystemsItem-based approaches based on SLIM (Sparse LInear Methods) have demonstrated very good performance for top-N recommendation; however they only estimate a single model for all the users. This work is based on the intuition that not all users behave in ...
Comments