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Paper Title:
Improving Personalized Services in Mobile Commerce by a Novel Multicriteria Rating Approach

Authors:

  • Qiudan Li(Chinese Academy of Sciences)
  • Chunheng Wang(Chinese Academy of Sciences)
  • Guanggang Geng(Chinese Academy of Sciences)

Abstract:
With the rapid growth of wireless technologies and mobile devices, there is a great demand for personalized services in m-commerce. Collaborative filtering (CF) is one of successful techniques to produce personalized recommendations for users. This paper proposes a novel approach to improve CF algorithms, where the contextual information of a user and the multicriteria ratings of an item are considered besides the typical information on users and items. The multilinear singular value decomposition (MSVD) technique is utilized to explore both explicit relations and implicit relations among user, item and criterion. We implement the approach in an existing m-commerce platform, and encouraging experimental results demonstrate its effectiveness.

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