Attribute Selection-Based Recommendation Framework for Long-Tail User Group: An Empirical Study on MovieLens Dataset

  • Jason J. Jung
  • Xuan Hau Pham
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6922)

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 long-tail user groups are useful for the recommendation process.

Keywords

User modeling Recommendation Long-tail group Attribute reduction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jason J. Jung
    • 1
  • Xuan Hau Pham
    • 1
  1. 1.Department of Computer EngineeringYeungnam UniversityGyeongsanKorea

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