Advertisement

Low-Rank and Sparse Cross-Domain Recommendation Algorithm

  • Zhi-Lin Zhao
  • Ling Huang
  • Chang-Dong Wang
  • Dong Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

Abstract

In this paper, we propose a novel Cross-Domain Collaborative Filtering (CDCF) algorithm termed Low-rank and Sparse Cross-Domain (LSCD) recommendation algorithm. Different from most of the CDCF algorithms which tri-factorize the rating matrix of each domain into three low dimensional matrices, LSCD extracts a user and an item latent feature matrix for each domain respectively. Besides, in order to improve the performance of recommendations among correlated domains by transferring knowledge and uncorrelated domains by differentiating features in different domains, the features of users are separated into shared and domain-specific parts adaptively. Specifically, a low-rank matrix is used to capture the shared feature subspace of users and a sparse matrix is used to characterize the discriminative features in each specific domain. Extensive experiments on two real-world datasets have been conducted to confirm that the proposed algorithm transfers knowledge in a better way to improve the quality of recommendation and outperforms the state-of-the-art recommendation algorithms.

Keywords

Low-rank Sparse Cross-domain Recommendation algorithm 

Notes

Acknowledgments

This work was supported by NSFC (61502543 & 61602189), Guangdong Natural Science Funds for Distinguished Young Scholar (2016A030306014), the Ph.D. Start-up Fund of Natural Science Foundation of Guangdong Province, China (2016A030310457), and Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (2016TQ03X542).

References

  1. 1.
    Zheng, B., Su, H., Zheng, K., Zhou, X.: Landmark-based route recommendation with crowd intelligence. Data Sci. Eng. 1(2), 86–100 (2016)CrossRefGoogle Scholar
  2. 2.
    Zhao, Z.L., Wang, C.D., Wan, Y.Y., Lai, J.H.: Recommendation in feature space sphere. Electron. Commer. Res. Appl. 26, 109–118 (2017)CrossRefGoogle Scholar
  3. 3.
    Hu, Q.Y., Zhao, Z.L., Wang, C.D., Lai, J.H.: An item orientated recommendation algorithm from the multi-view perspective. Neurocomputing 269, 261–272 (2017)CrossRefGoogle Scholar
  4. 4.
    Jannach, D., Zanker, M., Felfering, A., Friedrich, G.: Recommender Systems: An Introduction. Addison Wesley Publishing, Boston (2013)Google Scholar
  5. 5.
    Zhao, Z.L., Wang, C.D., Lai, J.H.: AUI&GIV: recommendation with asymmetric user influence and global importance value. PLoS ONE 11(2), e0147944 (2016)CrossRefGoogle Scholar
  6. 6.
    Allab, K., Labiod, L., Nadif, M.: A Semi-NMF-PCA unified framework for data clustering. IEEE Trans. Knowl. Data Eng. 29(1), 2–16 (2017)CrossRefGoogle Scholar
  7. 7.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2007)Google Scholar
  8. 8.
    Guo, G., Zhang, J., Thalmann, D.: Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowl.-Based Syst. 57, 57–68 (2014)CrossRefGoogle Scholar
  9. 9.
    Tan, B., Song, Y., Zhong, E., Yang, Q.: Transitive transfer learning. In: KDD, pp. 1155–1164 (2015)Google Scholar
  10. 10.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287–296 (2011)Google Scholar
  11. 11.
    Wei, S., Zheng, X., Chen, D., Chen, C.: A hybrid approach for movie recommendation via tags and ratings. Electron. Commer. Res. Appl. 18, 83–94 (2016)CrossRefGoogle Scholar
  12. 12.
    Xin, X., Liu, Z., Lin, C.Y., Huang, H., Wei, X., Guo, P.: Cross-domain collaborative filtering with review text. In: IJCAI, pp. 1827–1834 (2015)Google Scholar
  13. 13.
    McAuley, J.J., Targett, C., Shi, Q., van den Hengel, A.: Image-based recommendations on styles and substitutes. In: SIGIR, pp. 43–52 (2016)Google Scholar
  14. 14.
    Song, T., Peng, Z., Wang, S., Fu, W., Hong, X., Yu, P.S.: Review-based cross-domain recommendation through joint tensor factorization. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10177, pp. 525–540. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55753-3_33CrossRefGoogle Scholar
  15. 15.
    Moreno, O., Shapira, B., Rokach, L., Shani, G.: TALMUD: transfer learning for multiple domains. In: CIKM, pp. 425–434 (2012)Google Scholar
  16. 16.
    Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., Zhu, C.: Personalized recommendation via cross-domain triadic factorization. In: WWW, pp. 595–606 (2013)Google Scholar
  17. 17.
    Gao, S., Luo, H., Chen, D., Li, S., Gallinari, P., Guo, J.: Cross-domain recommendation via cluster-level latent factor model. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8189, pp. 161–176. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40991-2_11CrossRefGoogle Scholar
  18. 18.
    Liu, Y.F., Hsu, C.Y., Wu, S.H.: Non-linear cross-domain collaborative filtering via hyper-structure transfer. In: ICML, pp. 1190–1198 (2015)Google Scholar
  19. 19.
    Chen, J., Zhou, J., Ye, J.: Integrating low-rank and group-sparse structures for robust multi-task learning. In: KDD, pp. 42–50 (2011)Google Scholar
  20. 20.
    Candes, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis. J. ACM 58(3), 1–39 (2011)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: KDD, pp. 650–658 (2008)Google Scholar
  22. 22.
    Iwata, T., Takeuchi, K.: Cross-domain recommendation without shared users or items by sharing latent vector distributions. In: AISTATS, pp. 379–387 (2015)Google Scholar
  23. 23.
    Ren, S., Gao, S., Liao, J., Guo, J.: Improving cross-domain recommendation through probabilistic cluster-level latent factor model. In: AAAI, pp. 4200–4201 (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Zhi-Lin Zhao
    • 1
  • Ling Huang
    • 1
  • Chang-Dong Wang
    • 1
  • Dong Huang
    • 2
  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  2. 2.College of Mathematics and InformaticsSouth China Agricultural UniversityGuangzhouChina

Personalised recommendations