Special Issue on Recent Advances in Techniques for Intelligent Systems; Guest Editors: Susan Haller and Ingrid RussellNo Access

PRESERVING RECOMMENDER ACCURACY AND DIVERSITY IN SPARSE DATASETS

DERRY O' SULLIVAN

Smart Media Institute, University College Dublin, Ireland

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BARRY SMYTH

Smart Media Institute, University College Dublin, Ireland

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, and

DAVID WILSON

Dept. of Software & Information Systems, University of North Carolina at Charlotte, USA

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https://doi.org/10.1142/S0218213004001491Cited by:15 (Source: Crossref)

Abstract

Recommender systems combine research from user profiling, information filtering and artificial intelligence to provide users with more intelligent information access. They have proven to be useful in a range of Internet and e-commerce applications. Recent research has shown that a content-based (or case-based) perspective on collaborative filtering for recommendation can provide significant benefits in decision support accuracy over traditional collaborative techniques, particularly as dataset sparsity increases. These benefits derive both from the use of more sophisticated case-based similarity metrics and from the proactive maintenance of item similarity knowledge using data mining. This article presents a natural next step in this ongoing research to improve the quality of recommender systems by validating these findings in the context of more complex models of collaborative filtering, as well as by demonstrating that such techniques also preserve recommendation diversity, one of the key issues affecting traditional recommender systems.

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