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1998
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Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System

9 years 1 months ago
Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System
Collaborative filtering systems help address information overload by using the opinions of users in a community to make personal recommendations for documents to each user. Many collaborative filtering systems have few user opinions relative to the large number of documents available. This sparsity problem can reduce the utility of the filtering system by reducing the number of documents for which the system can make recommendations and adversely affecting the quality of recommendations. This paper defines and implements a model for integrating content-based ratings into a collaborative filtering system. The filterbot model allows collaborative filtering systems to address sparsity by tapping the strength of content filtering techniques. We identify and evaluate metrics for assessing the effectiveness of filterbots specifically, and filtering system enhancements in general. Finally, we experimentally validate the filterbot approach by showing that even simple filterbots such as spell ...
Badrul M. Sarwar, Joseph A. Konstan, Al Borchers,
Added 05 Aug 2010
Updated 05 Aug 2010
Type Conference
Year 1998
Where CSCW
Authors Badrul M. Sarwar, Joseph A. Konstan, Al Borchers, Jonathan L. Herlocker, Bradley N. Miller, John Riedl
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