Attribute selection-based recommendation framework for short-head user group: An empirical study by MovieLens and IMDB ?
Most of recommender 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.
recommender system user modeling long-tail group rough set attribute reduction