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Incorporating Social Information in Recommender Systems Using Hidden Markov Model

  • Jia-xin Zhang
  • Jin TianEmail author
Conference paper

Abstract

User preference always changes over time, which makes time the strong context information in the recommender system. Many time-dependent recommender systems have been proposed to track the change of users’ preferences. However, the social factor, which has been proved useful for recommender systems, is rarely considered in these models. In this paper, we consider the effects of social friends on the users’ behavior and propose a dynamic recommender system based on the hidden Markov model to provide better recommendations for users. We compare the proposed model with the traditional static and dynamic recommendation methods on real datasets and the experimental results show that the proposed model outperforms the compared methods.

Keywords

Recommender system Social information Hidden Markov model 

Notes

Acknowledgements

The work was supported by the General Program of the National Science Foundation of China (Grant No. 71471127, 71502125).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Management and EconomicsTianjin UniversityTianjinChina

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