Cross-domain recommendation based on latent factor alignment

Abstract

Recently, various cross-domain recommendation (CDR) models are proposed to overcome the sparsity problem, which leverage relatively abundant rating data from the auxiliary domain to improve recommendation performance of target domain. Though matrix factorization-based collaborative filtering algorithms gain dominance in single-domain recommendation systems, they cannot be used directly in cross-domain cases as the obtained latent factors of the target and auxiliary domains may not be aligned, which will lead to inaccurate knowledge transfer from the auxiliary domain to the target one. A CDR model named CDCFLFA is presented in this paper to solve this problem. In CDCFLFA, firstly latent factors between the two domains are aligned based on pattern matching. Then, user preferences of the auxiliary domain are transferred to update the original user latent vectors of target domain. Finally, a linear least square problem is solved to compute the item latent vectors of target domain and thus unknown ratings are obtained according to the updated user and item latent vectors. CDCFLFA does not require the same user or item sets between the two domains. Extensive experiments are conducted, and the results show that CDCFLFA achieves smaller MAE and RMSE values and larger precision and recall than the previous single- and cross-domain recommendation methods. Hence, CDCFLFA can be regarded as an effective cross-domain extension of single-domain matrix factorization algorithm.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 1.

    Bobadilla J, Ortega F, Hernando A et al (2013) Recommender systems survey. Knowl-Based Syst 46(1):109–132

    Article  Google Scholar 

  2. 2.

    Goldberg D, Nichols D, Oki BM et al (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70

    Article  Google Scholar 

  3. 3.

    Xu L, Wang H, Aaron Gulliver T (2020) Outage probability performance analysis and prediction for mobile IoV networks based on ics-bp neural network. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.3023694

    Article  Google Scholar 

  4. 4.

    Xu L, Wang J, Wang H, Aaron Gulliver T, Le KN (2020) BP neural network-based ABEP performance prediction for mobile Internet of Things communication systems. Neural Comput Appl 32(20):16025–16041

    Article  Google Scholar 

  5. 5.

    Liu L, Chen C, Pei Q et al (2020) Vehicular edge computing and networking: a survey. Mobile Netw Appl. https://doi.org/10.1007/s11036-020-01624-1

    Article  Google Scholar 

  6. 6.

    Liu L, Feng J, Pei O, Chen C, Ming Y, Shang B, Dong M (2020) Blockchain-enabled secure data sharing scheme in mobile edge computing: an asynchronous advantage actor-critic learning approach. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.3048345

    Article  Google Scholar 

  7. 7.

    Balabanović M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66–72

    Article  Google Scholar 

  8. 8.

    Resnick P, Iacovou N, Suchak M, et al. (1994) GroupLens: an open architecture for collaborative filtering of netnews. In: ACM conference on Computer supported cooperative work, pp 175–186

  9. 9.

    Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  10. 10.

    Yu X, Chu Y, Jiang F, Guo Y, Gong D (2018) SVMs classification based two-side cross domain collaborative filtering by inferring intrinsic user and item features. Knowl Based Syst 141:80–91

    Article  Google Scholar 

  11. 11.

    Yu X, Jiang F, Du J, Gong D (2019) A cross-domain collaborative filtering algorithm with expanding user and item features via the latent factor space of auxiliary domains. Pattern Recogn 94:96–109

    Article  Google Scholar 

  12. 12.

    Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. In: The 21th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1235–1244

  13. 13.

    Elkahky AM, Song Y, He X (2015) A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: International Conference on World Wide Web, pp 278–288

  14. 14.

    Cremonesi P, Tripodi A, Turrin R (2012) Cross-domain recommender systems. In: IEEE International Conference on Data Mining Workshops, pp 496–503

  15. 15.

    Li B, Yang Q, Xue X (2009) Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction. In: The International Joint Conference on Artificial Intelligence, pp 2052–2057

  16. 16.

    Li B, Yang Q, Xue X (2009) Transfer learning for collaborative filtering via a rating-matrix generative model. In: International Conference on Machine Learning, pp 617–624

  17. 17.

    Gao S, Luo H, Chen D et al (2014) A cross-domain recommendation model for cyber-physical systems. IEEE Trans Emerg Topics Comput 1(2):384–393

    Article  Google Scholar 

  18. 18.

    Lawson CL, Hanson RJ (1974) Solving least squares problems. Prentice-Hall, New Jersey

    Google Scholar 

  19. 19.

    Berkovsky S, Kuflik T, Ricci F (2007) Cross-domain mediation in collaborative filtering. In: International Conference on User Modeling, pp 355–359

  20. 20.

    Singh A P, Kumar G, Gupta R (2008) Relational learning via collective matrix factorization. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 650–658

  21. 21.

    Hu L, Cao J, Xu G, et al. (2013) Personalized recommendation via cross-domain triadic factorization. In: 22nd International Conference on World Wide Web, pp 595–606

  22. 22.

    Loni B, Shi Y, Larson M, et al. (2014) Cross-domain collaborative filtering with factorization machines. In: The 36th European Conference on Information Retrieval pp 656–661

  23. 23.

    Rendle S (2012) Factorization machines with libFM. ACM Trans Intell Syst Technol 3(3):1–22

    Article  Google Scholar 

  24. 24.

    Yu X, Jiang F, Du J et al (2017) A user-based cross domain collaborative filtering algorithm based on a linear decomposition model. IEEE Access 5(1):27582–27589

    Article  Google Scholar 

  25. 25.

    Caruana R (1997) Multitask learning. Mach Learn 28(1):41–75

    MathSciNet  Article  Google Scholar 

  26. 26.

    Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International Joint Conference on Artificial Intelligence, pp. 1137–1143.

  27. 27.

    Lay DC (2011) Linear algebra and its applications. Pearson Education, New York

    Google Scholar 

  28. 28.

    Golub GH, Van Loan CF (1996) Matrix computations, 3rd edn. Johns Hopkins University Press, Baltimore

    Google Scholar 

  29. 29.

    Ma H, Zhou D, Liu C, et al. (2011) Recommender systems with social regularization. In: The fourth ACM International Conference on Web Search and Data Mining, 287–296.

  30. 30.

    Yin H, Cui B, Li J et al (2012) Challenging the long tail recommendation. Proc VLDB Endow 5(9):896–907

    Article  Google Scholar 

Download references

Acknowledgements

This work is jointly sponsored by National Natural Science Foundation of China (Nos. 61402246, 61273180, 61702295, 61973180), Natural Science Foundation of Shandong Province (Nos. ZR2019MF014, ZR2019MF033), and key research and development program of Shandong Province (No. 2018GGX101052).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Lijun Sun.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yu, X., Hu, Q., Li, H. et al. Cross-domain recommendation based on latent factor alignment. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-021-05737-w

Download citation

Keywords

  • Cross-domain recommendation
  • Latent factor alignment
  • Linear least square problem
  • Transfer learning