Learning User Similarity and Rating Style for Collaborative Recommendation

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.

[1]  Naoki Abe,et al.  Collaborative Filtering Using Weighted Majority Prediction Algorithms , 1998, ICML.

[2]  Michael W. Berry,et al.  Mining consumer product data via latent semantic indexing , 1999, Intell. Data Anal..

[3]  Thomas Hofmann,et al.  Latent Class Models for Collaborative Filtering , 1999, IJCAI.

[4]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[5]  Bradley N. Miller,et al.  Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system , 1998, CSCW '98.

[6]  Michael J. Pazzani,et al.  Learning Collaborative Information Filters , 1998, ICML.

[7]  Philip S. Yu,et al.  Horting hatches an egg: a new graph-theoretic approach to collaborative filtering , 1999, KDD '99.

[8]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[9]  Gediminas Adomavicius,et al.  Expert-Driven Validation of Rule-Based User Models in Personalization Applications , 2004, Data Mining and Knowledge Discovery.

[10]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

[11]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[12]  T. Joachims.  WebWatcher : A Tour Guide for the World Wide Web , 1997 .

[13]  Thorsten Joachims,et al.  Web Watcher: A Tour Guide for the World Wide Web , 1997, IJCAI.

[14]  Daniel A. Keim,et al.  Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , 2002, KDD.

[15]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[16]  Hans-Peter Kriegel,et al.  Feature Weighting and Instance Selection for Collaborative Filtering: An Information-Theoretic Approach* , 2003, Knowledge and Information Systems.

[17]  David McSherry,et al.  Diversity-Conscious Retrieval , 2002, ECCBR.

[18]  Barry Smyth,et al.  Improving Recommendation Diversity , 2001 .

[19]  Henry Lieberman,et al.  Letizia: An Agent That Assists Web Browsing , 1995, IJCAI.

[20]  Barry Smyth,et al.  Case-Based User Profiling for Content Personalisation , 2000, AH.

[21]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

[22]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[23]  Ralph Bergmann,et al.  Diversity-Conscious Retrieval from Generalized Cases: A Branch and Bound Algorithm , 2003, ICCBR.

[24]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[25]  John Riedl,et al.  Combining Collaborative Filtering with Personal Agents for Better Recommendations , 1999, AAAI/IAAI.

[26]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[27]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[28]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[29]  Yoav Shoham,et al.  Content-Based, Collaborative Recommendation. , 1997 .

[30]  Christian Posse,et al.  Bayesian Mixed-Effects Models for Recommender Systems , 1999 .

[31]  Jiming Liu,et al.  An Adaptive User Interface Based On Personalized Learning , 2003, IEEE Intell. Syst..

[32]  Jiming Liu,et al.  Extended latent class models for collaborative recommendation , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[33]  Bradley N. Miller,et al.  Applying Collaborative Filtering to Usenet News , 1997 .

[34]  A. L. Barker,et al.  Selection of distance metrics and feature subsets for K-nearest neighbor classifiers , 1997 .