Using Filtering Agents to Improve Prediction Quality in GroupLens Research Collaborative Filtering System

Abstract

Collaborative filtering systems help address information overload by usingthe opinions of users in a community to make personal recommendations fordocuments to each user. Many collaborative filtering systems have few useropinions relative to the large number of documents available. This sparsityproblem can reduce the utility of the filtering system by reducing thenumber of documents for which the system can make recommendations andadversely affecting the quality of recommendations. This paper defines andimplements a model for integrating content-based ratings into acollaborative filtering system. The filterbot model allows collaborativefiltering systems to address sparsity by tapping the strength of contentfiltering techniques. We identify and evaluate metrics for assessing theeffectiveness of filterbots specifically, and filtering system enhancementsin general. Finally, we experimentally validate the filterbot approach byshowing that even simple filterbots such as spell checking can increase theutility for users of sparsely populated collaborative filtering systems.Keywords Collaborative filtering, information filtering, content analysis,recommendation systems, social filtering, GroupLens Research, informationfiltering agents.