Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation

Authors

  • Hui Fang Nanyang Technological University, Singapore
  • Yang Bao Nanyang Technological University
  • Jie Zhang Nanyang Technological University

DOI:

https://doi.org/10.1609/aaai.v28i1.8714

Abstract

Trust has been used to replace or complement rating-based similarity in recommender systems, to improve the accuracy of rating prediction. However, people trusting each other may not always share similar preferences. In this paper, we try to fill in this gap by decomposing the original single-aspect trust information into four general trust aspects, i.e. benevolence, integrity, competence, and predictability, and further employing the support vector regression technique to incorporate them into the probabilistic matrix factorization model for rating prediction in recommender systems. Experimental results on four datasets demonstrate the superiority of our method over the state-of-the-art approaches.

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Published

2014-06-19

How to Cite

Fang, H., Bao, Y., & Zhang, J. (2014). Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8714