Abstract
Data sparsity is one of the most challenging problems in recommender systems. In this paper, we tackle the data sparsity problem by proposing a heterogeneous context-aware semi-supervised tensor factorization method named HASS. Firstly, heterogeneous context are classified and processed by different modeling approaches. We use a tensor factorization model to capture user-item interaction contexts and use a matrix factorization model to capture both user attributed contexts and item attributed contexts. Secondly, different context models are optimized with semi-supervised co-training approach. Finally, the two sub-models are combined effectively by an weight fusing method. As a result, the HASS method has several distinguished advantages for mitigating the data sparsity problem. One is that the method can well perceive diverse influences of heterogeneous contexts and another is that a large number of unlabeled samples can be utilized by the co-training stage to further alleviate the data sparsity problem. The proposed algorithm is evaluated on real-world datasets and the experimental results show that HASS model can significantly improve recommendation accuracy by comparing with the existing state-of-art recommendation algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender Systems Handbook, pp. 191–226. Springer, Boston (2015)
Zhang, M., Tang, J., Zhang, X., et al.: Addressing cold start in recommender systems: A semi-supervised co-training algorithm. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 73–82. (2014)
Liu, Q., Wu, S., Wang, L.: COT: contextual operating tensor for context-aware recommender systems. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 203–209 (2015)
Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N.: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems - RecSys 2010 (2010)
Rendle, S., Gantner, Z., Freudenthaler, C., Schmidt-Thieme, L.: Fast context-aware recommendations with factorization machines. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information - SIGIR 2011, pp. 635–644 (2011)
Wu, S., Liu, Q., Wang, L., Tan, T.: Contextual operation for recommender systems. IEEE Trans. Knowl. Data Eng. 28(8), 2000–2012 (2016)
Liu, Q., et al.: Context-aware sequential recommendation. In: IEEE 16th International Conference on Data Mining (ICDM) (2016)
Qu, W., Song, K.-S., Zhang, Y.-F., Feng, S., Wang, D.-L., Yu, G.: A novel approach based on multi-view content analysis and semi-supervised enrichment for movie recommendation. J. Comput. Sci. Technol. 28(5), 776–787 (2013)
Acknowledgments
This work is supported by Chinese National Science Foundation (#61763007), Guangxi Key Lab of Trusted Software under project Kx201503 and Innovation Project of GUET Graduate Education (#2017YJCX44).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Cai, G., Gu, W. (2017). Heterogeneous Context-aware Recommendation Algorithm with Semi-supervised Tensor Factorization. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-68935-7_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68934-0
Online ISBN: 978-3-319-68935-7
eBook Packages: Computer ScienceComputer Science (R0)