Attribute selection-based recommendation framework for long-tail user group: an empirical study on movieLens dataset

Authors:
Jason J. Jung;Xuan Hau Pham
Affiliations:
Department of Computer Engineering, Yeungnam University, Dae-Dong, Gyeongsan, Korea;Department of Computer Engineering, Yeungnam University, Dae-Dong, Gyeongsan, Korea
Venue:
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Year:
2011

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Abstract

Most of recommendation systems have serious difficulties on providing relevant services to the "short-head" users who have shown intermixed preferential patterns. In this paper, we assume that such users (which are referred to as long-tail users) can play an important role of information sources for improving the performance of recommendation. Attribute reduction-based mining method has been proposed to efficiently select the long-tail user groups. More importantly, the long-tail user groups as domain experts are employed to provide more trustworthy information. To evaluate the proposed framework, we have integrated MovieLens dataset with IMDB, and empirically shown that the longtail user groups are useful for the recommendation process.