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.
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Acknowledgements
The work was supported by the General Program of the National Science Foundation of China (Grant No. 71471127, 71502125).
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Zhang, Jx., Tian, J. (2019). Incorporating Social Information in Recommender Systems Using Hidden Markov Model. In: Huang, G., Chien, CF., Dou, R. (eds) Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018. Springer, Singapore. https://doi.org/10.1007/978-981-13-3402-3_21
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DOI: https://doi.org/10.1007/978-981-13-3402-3_21
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