Feature Weighting and Instance Selection for Collaborative Filtering: An Information-Theoretic Approach

Authors:
Kai Yu;Xiaowei Xu;Martin Ester;Hans-Peter Kriegel
Affiliations:
Siemens AG, Corporate Technology, Germany and Institute for Computer Science, University of Munich, Munich, Germany;Siemens AG, Corporate Technology, Germany and Information Science Department, University of Arkansas at Little Rock, Little Rock, Arkansas, USA;University of Munich, Institute for Computer Science, Germany;University of Munich, Institute for Computer Science, Germany
Venue:
Knowledge and Information Systems
Year:
2003

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Abstract

Collaborative filtering (CF) employing a consumer preference database to make personal product recommendations is achieving widespread success in E-commerce. However, it does not scale well to the ever-growing number of consumers. The quality of the recommendation also needs to be improved in order to gain more trust from consumers. This paper attempts to improve the accuracy and efficiency of collaborative filtering. We present a unified information-theoretic approach to measure the relevance of features and instances. Feature weighting and instance selection methods are proposed for collaborative filtering. The proposed methods are evaluated on the well-known EachMovie data set and the experimental results demonstrate a significant improvement in accuracy and efficiency.