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
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
A. Tuzhilin, Towards the next generation of recommender systems, in Proceedings of the 1st International Conference on E-Business Intelligence (ICEBI2010), pp. 734–749
R.J. Mooney, L. Roy, Content-based book recommending using learning for text categorization, in Proceedings of the Fifth ACM Conference on Digital Libraries (2000), pp. 195–204
G. Guo, Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems, in Proceedings of the 7th ACM conference on Recommender systems (RecSys’13) (2013), pp. 451–454
C. Chen, X. Zheng, Y. Wang, F. Hong, Z. Lin, Context-ware collaborative topic regression with social matrix factorization for recommender systems, in AAAI’14 Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (2014), pp. 9–15
X. Ren, M. Song, E. Haihong, J. Song, Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation. Neurocomputing 38–55 (2017)
Y. Koren, Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)
H. Bao, Q. Li, S.S. Liao, S. Song, H. Gao, A new temporal and social PMF-based method to predict users’ interests in micro-blogging. Decis. Support Syst. 55(3), 698–709 (2013)
L. Xiang et al., Temporal recommendation on graphs via long- and short-term preference fusion, in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2010), pp. 723–732
K. Inuzuka, T. Hayashi, T. Takagi, Recommendation system based on prediction of user preference changes, in International Conference on Web Intelligence (2017), pp. 192–199
M. Hosseinzadeh Aghdam et al., Adapting recommendations to contextual changes using hierarchical hidden markov models, in Proceedings of the 9th ACM Conference on Recommender Systems (2015), pp. 241–244
L.E. Baum, Statistical inference for probabilistic functions of finite Markov chains. Ann. Math. Stat. 37(6), 1554–1563 (1966)
N. Sahoo, P.V. Singh, T. Mukhopadhyay, A hidden Markov model for collaborative filtering. Manage. Inf. Syst. Q. 36(4), 1329–1356 (2012)
A.P. Dempster, Maximum likelihood from incomplete data via EM algorithm. J. R. Stat. Soc. B 39, 1–38 (1977)
T. Hofmann, J. Puzicha, Latent class models for collaborative filtering, in Sixteenth International Joint Conference on Artificial Intelligence (1990), pp. 688–693
Acknowledgements
The work was supported by the General Program of the National Science Foundation of China (Grant No. 71471127, 71502125).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-13-3402-3_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3401-6
Online ISBN: 978-981-13-3402-3
eBook Packages: Business and ManagementBusiness and Management (R0)