Abstract
Traditional collaborative filtering (CF) recommendation algorithms usually use a single rating to recommend items to users, which works well in terms of predictive accuracy. However, recent research on multi-criteria recommender has shown that multi-criteria ratings are of great value to improving recommendation performance. In this paper, we present novel multi-criteria recommendation schemes which leverage multi-criteria ratings and codebook cluster information derived from user-item-criteria ratings matrix to enhance recommendation quality. Particularly, we utilize Factorization Machines (FMs) to integrate the codebook clusters information on individual criteria, which contains users’ preferences on different criteria of items, to extend user-item-criteria interaction feature vectors and make an overall rating prediction. A set of experiments on a real-world datasets show that our approach outperforms both FMs-based single-rating recommendation algorithms in which the clusters information of users or items are based on an overall rating, as well as three existing state-of-the-art multi-criteria recommendation algorithms even in case where data are under high sparsity.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adomavicius, G., Tuzhilin, A.: Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Adomavicius, G., Manouselis, N., Kwon, Y.: Multi-criteria recommender systems. In: Recommender Systems Handbook, chapter (12), pp. 769–803 (2011)
Adomavicius, G., Kwon, Y.: New recommendation techniques for multi-criteria rating systems. IEEE Intell. Syst. 22(3), 48–55 (2007)
Liu, L., Mehandjiev, N., Xu, D.: Multi-criteria service recommendation based on user criteria preferences. In: Proceedings of the fifth ACM Conference on Recommender Systems, pp. 77–84 (2011)
Liu, L., Mehandjiev, N., Xu, D.: Multi-criteria service recommendation based on user criteria preferences. In: Proceedings of the fifth ACM Conference on Recommender Systems, pp. 77–84 (2011)
Jannach, D., Karakay, Z., Gedikl, F.: Accuracy improvements for multi-criteria recommender systems. In: Proceedings of the 12th ACM Conference on Electronic Commerce, pp. 674–689 (2012)
Fan, J., Xu, L.: A robust multi-criteria recommendation approach with preference-based similarity and support vector machine. In: Proceedings of the 10th International Symposium on Neural Networks, Part (2), pp. 385–394 (2013)
Sahoo, N., Krishnan, R., Duncan, G., et al.: The Halo Effect in multicomponent ratings and its implications for recommender systems: the case of Yahoo! movies. Inf. Syst. Res. 23(1), 231–246 (2011)
Samatthiyadikun, P., Takasu, A., Maneeroj, S.: Multicriteria collaborative filtering by Bayesian model-based user profiling. In: Proceedings of IEEE 13th International Conference on Information Reuse and Integration, pp. 124–131 (2012)
Samatthiyadikun, P., Takasu, A., Maneeroj, S.: Bayesian model for a multicriteria recommender system with support vector regression. In: Proceedings of IEEE International Conference on Information Reuse and Integration, pp. 38–45 (2013)
Sreepada, R.S., Patra, B.K., Hernando, A. Multi-criteria recommendations through preference learning. ACM Ikdd Conferences, pp. 1–11 (2017)
Agathokleous, M., Tsapatsoulis, N.: Learning user models in multi-criteria recommender systems. Commun. Comput. Inf. Sci. 459, 205–216 (2014)
Zheng, Y.: Criteria chains: a novel multi-criteria recommendation approach. In: International Conference on Intelligent User Interfaces, pp. 29–33 (2017)
Rendle, S.: Factorization machines. In: Proceedings of the IEEE International Conference on Data Mining, pp. 995–1000 (2010)
Hong, L., Doumith, A.S., Davison, B.D. Co-factorization machines: modeling user interests and predicting individual decisions in Twitter. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 557–566 (2013)
Loni, B., Shi, Y., Larson, M., et al.: Cross-domain collaborative filtering with factorization machines. In: Proceedings of the 36th European Conference on IR Research, pp. 656–661 (2014)
Qiang, R., Liang, F., Yang, J.: Exploiting ranking factorization machines for microblog retrieval. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 1783–1788 (2013)
Oentaryo, R.J., Lim, E.-P., Low, J.-W.: Predicting response in mobile advertising with hierarchical importance-aware factorization machine. In: Proceedings of the Seventh ACM International Conference on Web Search and Data Mining, pp. 123–132 (2014)
Loni, B., Said, A., Larson, M., et al. In: Free lunch’ enhancement for collaborative filtering with factorization machines. In: Proceeding of the Eighth ACM Conference on Recommender Systems, pp. 281–284 (2014)
Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3(57), 1–22 (2012)
Acknowledgements
This work was financially supported by the National Natural Science Foundation of China (Grant: 61502350) and the Joint Funds of National Natural Science foundation of China (Grant: U1536114).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ding, Y., Li, S. & Yu, W. Multi-criteria recommendation schemes based on factorization machines. Cluster Comput 22 (Suppl 6), 14419–14426 (2019). https://doi.org/10.1007/s10586-018-2308-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-018-2308-7