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A Deep Hybrid Collaborative Filtering Based on Multi-dimension Analysis

  • Chunyan Zeng
  • Songnan Lv
  • Shangli ZhouEmail author
  • Zhifeng Wang
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 97)

Abstract

In order to solve the problem that the existing neural collaborative filtering methods are not comprehensive to mine the latent information of embedded vectors, a deep hybrid collaborative filtering based on multi-dimension analysis is proposed. The idea is to use different feature fusion methods for the embedded vectors of users and items to obtain multiple dimensional fusion features, so that the information explored by different methods can complement each other, and the model can better discover the interaction between users and items. Experimental results show that, compared with the single-method of dimension analysis, the multi-dimension analysis can effectively improve the model’s ability to mine the interaction between users and items, and improve the performance of the recommender system.

Notes

Acknowledgments

This research was supported by National Natural Science Foundation of China (No. 61901165, No. 61501199), Excellent Young and Middle-aged Science and Technology Innovation Team Project in Higher Education Institutions of Hubei Province (No. T201805), Hubei Natural Science Foundation (No. 2017CFB683), and self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (No. CCNU18QN021).

References

  1. 1.
    Zhang, H., et al.: Discrete collaborative filtering. In: The 39th International ACM SIGIR Conference. ACM (2016)Google Scholar
  2. 2.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, 24–27 August. ACM (2008)Google Scholar
  3. 3.
    He, X., et al.: Fast matrix factorization for online recommendation with implicit feedback (2017)Google Scholar
  4. 4.
    Rendle, S.: Factorization machines. In: IEEE International Conference on Data Mining. IEEE (2011)Google Scholar
  5. 5.
    He, X., et al.: Neural collaborative filtering (2017)Google Scholar
  6. 6.
    He, X., et al.: Outer product-based neural collaborative filtering (2018)Google Scholar
  7. 7.
    Liang, D., et al.: Modeling user exposure in recommendation (2015)Google Scholar
  8. 8.
    He, X., Chua, T.-S.: Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (2017)Google Scholar
  9. 9.
    He, X., et al.: TriRank: review-aware explainable recommendation by modeling aspects. In: The 24th ACM International. ACM (2015)Google Scholar
  10. 10.
    Sarwar, B., et al.: Item-based collaborative filtering recommendation algorithms. In: International Conference on World Wide Web (2001)Google Scholar
  11. 11.
    Rendle, S., et al.: BPR: Bayesian personalized ranking from implicit feedback (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Chunyan Zeng
    • 1
  • Songnan Lv
    • 1
  • Shangli Zhou
    • 1
    Email author
  • Zhifeng Wang
    • 2
  1. 1.Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage SystemHubei University of TechnologyWuhanChina
  2. 2.Department of Digital Media TechnologyCentral China Normal UniversityWuhanChina

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