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Cluster Computing

, Volume 22, Supplement 6, pp 14419–14426 | Cite as

Multi-criteria recommendation schemes based on factorization machines

  • Yonggang Ding
  • Shijun LiEmail author
  • Wei Yu
Article
  • 120 Downloads

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.

Keywords

Factorization machines (FMs) Multi-criteria recommendation Codebook cluster 

Notes

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).

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Adomavicius, G., Manouselis, N., Kwon, Y.: Multi-criteria recommender systems. In: Recommender Systems Handbook, chapter (12), pp. 769–803 (2011)Google Scholar
  3. 3.
    Adomavicius, G., Kwon, Y.: New recommendation techniques for multi-criteria rating systems. IEEE Intell. Syst. 22(3), 48–55 (2007)CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Sreepada, R.S., Patra, B.K., Hernando, A. Multi-criteria recommendations through preference learning. ACM Ikdd Conferences, pp. 1–11 (2017)Google Scholar
  12. 12.
    Agathokleous, M., Tsapatsoulis, N.: Learning user models in multi-criteria recommender systems. Commun. Comput. Inf. Sci. 459, 205–216 (2014)Google Scholar
  13. 13.
    Zheng, Y.: Criteria chains: a novel multi-criteria recommendation approach. In: International Conference on Intelligent User Interfaces, pp. 29–33 (2017)Google Scholar
  14. 14.
    Rendle, S.: Factorization machines. In: Proceedings of the IEEE International Conference on Data Mining, pp. 995–1000 (2010)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    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)Google Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    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)Google Scholar
  20. 20.
    Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3(57), 1–22 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ComputerWuhan UniversityWuhanChina
  2. 2.School of EducationHubei UniversityWuhanChina

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