Abstract
Temporal factor plays an important role in products and services recommended process. It is necessary to combine temporal factors with effective methods to improve recommendation performance. In this paper, we present a novel approach to improve personalized recommendation performance with changing user preferences based on temporal dataset. In the approach, we take consideration of different influence of long and short-term user preferences and construct a preference transfer model based on our enhanced Hidden Markov Model. Then we accomplish preference fusion by adopting our Long and Short Term Graph, a graph model modified from Session-based Temporal Graph, to recommend unknown items. Finally, the experimental results show that our approach achieves important improvements compared to some existing approaches in performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sarwar, B., Karypis, G., Konstan, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, Hong Kong, pp. 285–295 (2001)
Koren, Y.: Collaborative filtering with temporal dynamics. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, pp. 89–295 (2009)
Peng, J., Zeng, D.D., Zhao, H.: Collaborative filtering in social tagging systems based on joint item-tag recommendations. In: ACM International Conference on Information and Knowledge Management, Toronto, pp. 809–818 (2010)
Liu, Q., Chen, E., Xiong, H.: Enhancing collaborative filtering by user interest expansion via personalized ranking. IEEE Trans. Syst. Man Cybern. Part B Cybern. A Publ. IEEE Syst. Man Cybern. Soc. 42, 218–233 (2012)
Popescul, A., Ungar, L.H., Pennock, D.M.: Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. ePrint arXiv, 437-444 (2013)
Ninaus, G., Reinfrank, F., Stettinger, M.: Content-based recommendation techniques for requirements engineering. In: 22nd IEEE International Conference on Requirements Engineering, pp. 1439–1446 (2014)
Shih, Y.Y., Liu, D.R.: Hybrid recommendation approaches: collaborative filtering via valuable content information. In: Proceedings of the 38th Annual Hawaii International Conference on IEEE, Hawaii, p. 271b (2005)
Rojsattarat, E., Soonthornphisaj, N.: Hybrid Recommendation: Combining Content-Based Prediction and Collaborative Filtering. Intelligent Data Engineering and Automated Learning, pp. 337–344 (2003)
Lathia, N., Hailes, S., Capra, L.: Temporal collaborative filtering with adaptive neighbourhoods. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 796–797. ACM (2009)
Adomavicius, G., Tuzhilin, A.: Context-Aware Recommender Systems Recommender Systems Handbook, pp. 217–253. Springer, US (2011)
Hosseinzadeh, A.M., Hariri, N., Mobasher, B.: Adapting recommendations to contextual changes using hierarchical hidden Markov models. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 241–244. ACM (2015)
Javari, A., Jalili, M.: A probabilistic model to resolve diversity-accuracy challenge of recommendation systems. Knowl. Inf. Syst. 44(3), 1–19 (2015)
Stefanidis, K., Pitoura, E., Vassiliadis, P.: Managing contextual preferences. J. Inf. Syst. 36(8), 1158–1180 (2011)
Sahoo, N., Singh, P.V., Mukhopadhyay, T.: A hidden Markov model for collaborative filtering. J. Ssrn Electron. J. 36(4), 1329–1356 (2010)
Xiang, L., Yuan, Q., Zhao, S.: Temporal recommendation on graphs via long-and short-term preference fusion. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 723–732 (2010)
Acknowledgement
The authors acknowledge the financial support of the Frontier and Application Foundation Research Program of CQ CSTC (No. cstc2014jcyjA40037), National High-tech R&D Program of China (No. 2015AA015308), the Chongqing Graduate Student Research Innovation Project (No. CYS16032), Fundamental Research Funds for the Central Universities (No. CDJZR188801), Degree and Graduate Education Research Program (No. 2015Y0414), and the Project of Chongqing Higher Education Reform (No. 153003). The author is grateful to the anonymous referee for a careful checking of the details and for helpful comments that improved this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, B., Feng, Y. (2016). Improving Temporal Recommendation Accuracy and Diversity via Long and Short-Term Preference Transfer and Fusion Models. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-45817-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45816-8
Online ISBN: 978-3-319-45817-5
eBook Packages: Computer ScienceComputer Science (R0)