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 long-tail user groups are useful for the recommendation process. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Jung, J. J., & Pham, X. H. (2011). Attribute selection-based recommendation framework for long-tail user group: An empirical study on MovieLens dataset. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6922 LNAI, pp. 592–601). https://doi.org/10.1007/978-3-642-23935-9_58
Mendeley helps you to discover research relevant for your work.