Abstract
Information filtering is an area getting more important as we have long been flooded with too much information, where product brokering in e-commerce is a typical example. Systems which can provide personalized product recommendations to their users (often called recommender systems) have gained a lot of interest in recent years. Collaborative filtering is one of the commonly used approaches which normally requires a definition of user similarity measure. In the literature, researchers have proposed different choices for the similarity measure using different approaches, and yet there is no guarantee for optimality. In this paper, we propose the use of machine learning techniques to learn the optimal user similarity measure as well as user rating styles for enhancing recommendation acurracy. Based on a criterion function measuring the overall prediction error, several ratings transformation functions for modeling rating styles together with their learning algorithms are derived. With the help of the formulation and the optimization framework, subjective components in user ratings are removed so that the transformed ratings can then be compared. We have evaluated our proposed methods using the EachMovie dataset and succeeded in obtaining significant improvement in recommendation accuracy when compared with the standard correlation-based algorithm.
Article PDF
Similar content being viewed by others
References
A Movies Recommender System, http://www.movielens.umn.edu (visited Sept. 15, 2003).
Adomavicius G and Tuzhilin A (2001) Expert-driven validation of rule-based user models in personalization applications. Data Mining and Knowledge Discovery, 5(1/2):33–58.
Aggarwal C,Wolf J,WuKandYu P (1999) Horting hatches an egg:Anewgraph-theoretic approach to collaborative filtering. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, pp. 201-212.
Balabanović M and Shoham Y (1997) Content-based, collaborative recommendation. Communications of the ACM, 40(3):66–72.
Barker AL (1997) Selection of distance metrics and feature subsets for kNN classifiers. Ph.D. thesis, Department of Computer Science, University of Virginia.
Basu C, Hirsh H and Cohen W (1998) Recommendation as classification: Using social and content-based information in recommendation. In: Recommender Systems-Papers from the AAAI Workshop. Madison, WI.
Billsus D and Pazzani M (1998) Learning collaborative information filter. In: Proceedings of the Fifteenth International Conference on Machine Learning. Madison, WI, pp. 46-54.
Bradley K, Rafter R and Smyth B (2000) Case-based user profiling for content personalization. In: Proceedings of the Internation Conference on Adaptive Hypermedia and Adaptive Web-Based Systems. Trento, Italy.
Bradley K and Smyth B (2001) Improving recommendation diversity. In: Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science. Maynooth, Ireland.
Breese J, Heckerman D and Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence. Madison, WI.
Cheung K, Tsui K and Liu J (2003) An extended latent class model for collaborative recommendation. IEEE Transactions on Systems, Man and Cybernetics: Part A (to appear).
Claypool M, Gokhale A and Miranda T (1999) Combining content-based and collaborative filters in an online newspaper. In: Proceedings of the SIGIR-99Workshop on Recommender Systems: Algorithms and Evaluation. Berkeley, CA.
Condliff M and Lewis D (1999) Bayesian mixed-effects models for recommender systems. In: Proceedings of the SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation. Berkeley, CA.
Dash M and Liu H (1997) Feature selection for classification: A survey. Intelligent Data Analysis, 1(3).
Good N, Schafer J, Konstan J, Borchers A, Sarwar B, Herlocker J and Riedl J (1999) Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of the 1999 Conference of the American Association of Artifical Intelligence (AAAI-99).
Hofmann T and Puzicha J (1999) Latent class models for collaborative filtering. In: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI99), pp. 688-693.
Jiang J, Berry M, Donato J, Ostrouchov G and Grady N (1999) Mining consumer product data via latent semantic indexing. Intelligent Data Analysis, 3:377–398.
Joachims T, Freitag D and Mitchell T (1997) WebWatcher: A tour guide for the World Wide Web. In: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI97), Nagoya, Japan.
Konstan J, Miller BN, Maltz D, Herlocker JL, Gordon L and Riedl J (1997) Applying collaborative filtering to Usenet news. Communications of the ACM 40(3):77–87.
Lieberman H (1995) Letizia: An agent that assist Web browsing. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI97). Montreal, Canada.
Liu J, Wong CK and Hui KK (2003) An adaptive user interface based on personalized learning. IEEE Intelligent Systems 18(2):52–57.
McSherry D (2002) Diversity-conscious retrieval. In: Advances in Case-Based Reasoning, Sixth European Conference on Case-Based Reasoning. Lecture Notes in Computer Science. Aberdeen, Scotland, UK, Springer, pp. 219–233.
Mooney R and Roy L (1999) Content-based book recommending using learning for text categorization. In: Proceedings of the SIGIR-99Workshop on Recommender Systems: Algorithms and Evaluation. Berkeley, CA.
Mougouie B, Richter M and Bergmann R (2003) Diversity-conscious retrieval from generlized cases: A branch and bound algorithm. In: Case-Based Reasoning Research and Development, Fifth International Conference on Case-Based Reasoning. Lecture Notes in Computer Science. Trondheim, Norway, Springer, pp. 319–331.
Nakamura A and Abe N (1998) Collaborative filtering using weighted majority prediction algorithms. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 395-403.
Pazzani M (1999) A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review.
Pazzani M and Billsus D (1997) Learning and revising user profiles: The identification of interesting Web sites. Machine Learning 27:313–331.
Personalization Consortium News, http://www.personalization.org/pr050901.html (published May 21, 2001).
Richardson M and Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proceedings of the 2002 International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada.
Sarwar B, Konstan J, Borchers A, Herlocker J, Miller B and Riedl J (1998) Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system. In: Proceedings of ACM 1998 Conference on Computer Supported Cooperative Work. Seattle, Washington.
Shardanand U and Maes P (1995) Social information filtering: Algorithms for automating 'word of mouth'. In: Proceedings of the Computer-Human Interaction Conference (CHI95). Denver, CO.
Yu K, Xu X, Ester M and Kriegel H-P (2003) Feature weighting and instance selection for collaborative filtering: An information-theoretic approach. Knowledge and Information Systems: An International Journal.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Cheung, KW., Tian, L.F. Learning User Similarity and Rating Style for Collaborative Recommendation. Information Retrieval 7, 395–410 (2004). https://doi.org/10.1023/B:INRT.0000011212.66249.b7
Issue Date:
DOI: https://doi.org/10.1023/B:INRT.0000011212.66249.b7