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DTMF: A User Adaptive Model for Hybrid Recommendation

  • Wenlong Yang
  • Jun MaEmail author
  • Shanshan Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)

Abstract

Due to the uneven distributions of the ratings and social relationships for each user, two types of above recommendation methods should have varying weights when make recommendations. In this paper, we propose a user adaptive hybrid recommendation model, which dynamically combines a trust-aware based method and low-rank matrix factorization with adaptive tradeoff parameters, named as DTMF. It can utilize the advantages of these two methods and learn combinative parameters automatically. We investigate our model’s performance on two social data sets - Epinions and Flixster. Experimental results show that DTMF performs better than other state-of-the-art methods.

Keywords

Recommender systemsa Trust-aware Matrix factorization 

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References

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    Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 135–142. ACM (2010)Google Scholar
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    Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems, RecSys 2007, pp. 17–24. ACM (2007)Google Scholar
  3. 3.
    Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina

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