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 checking can increase the utility for users of sparsely populated collaborative filtering systems.
|Number of pages
|Published - 1998
|Proceedings of the 1998 7th ACM Conference on Computer Supported Cooperative Work, CSCW'98 - Seattle, WA, USA
Duration: Nov 14 1998 → Nov 18 1998
|Proceedings of the 1998 7th ACM Conference on Computer Supported Cooperative Work, CSCW'98
|Seattle, WA, USA
|11/14/98 → 11/18/98