Skip to main content

Cross-Domain Recommendation for Cold-Start Users via Neighborhood Based Feature Mapping

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2018)

Abstract

Traditional Collaborative Filtering (CF) models mainly focus on predicting a user’s preference to the items in a single domain such as the movie domain or the music domain. A major challenge for such models is the data sparsity problem, and especially, CF cannot make accurate predictions for the cold-start users who have no ratings at all. Although Cross-Domain Collaborative Filtering (CDCF) is proposed for effectively transferring users’ rating preference across different domains, it is still difficult for existing CDCF models to tackle the cold-start users in the target domain due to the extreme data sparsity. In this paper, we propose a Cross-Domain Latent Feature Mapping (CDLFM) model for cold-start users in the target domain. Firstly, the user rating behavior is taken into consideration in the matrix factorization for alleviating the data sparsity. Secondly, neighborhood based latent feature mapping is proposed to transfer the latent features of a cold-start user from the auxiliary domain to the target domain. Extensive experiments on two real datasets extracted from Amazon transaction data demonstrate the superiority of our proposed model against other state-of-the-art methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Pan, W., Yang, Q.: Transfer learning in heterogeneous collaborative filtering domains. Artif. Intell. 197, 39–55 (2013)

    Article  MathSciNet  Google Scholar 

  2. Pan, W., Xiang, E.W., Liu, N.N., Yang, Q.: Transfer learning in collaborative filtering for sparsity reduction. In: AAAI, pp. 230–235 (2010)

    Google Scholar 

  3. Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: KDD, pp. 650–658 (2008)

    Google Scholar 

  4. Wang, X., Peng, Z., Wang, S., Yu, P.S., Fu, W., Hong, X.: Cross-domain recommendation for cold-start users via neighborhood based feature mapping. https://arxiv.org/

  5. Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2007)

    Google Scholar 

  6. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD, pp. 426–434 (2008)

    Google Scholar 

  7. Zhen, Y., Li, W.J., Yeung, D.Y.: TagiCoFi: tag informed collaborative filtering. In: RecSys, pp. 69–76 (2009)

    Google Scholar 

  8. Fernández-Tobías, I., Cantador, I.: Exploiting social tags in matrix factorization models for cross-domain collaborative filtering. In: CBRecSys@RecSys, pp. 34–41 (2014)

    Google Scholar 

  9. 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_33

    Chapter  Google Scholar 

  10. 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 

  11. Man, T., Shen, H., Jin, X., Cheng, X.: Cross-domain recommendation: an embedding and mapping approach. In: IJCAI, pp. 2464–2470 (2017)

    Google Scholar 

  12. Kazama, M., Varga, I.: Cross domain recommendation using vector space transfer learning. In: RecSys Posters (2016)

    Google Scholar 

  13. Wang, S., Hu, X., Yu, P.S., Li, Z.: MMRate: inferring multi-aspect diffusion networks with multi-pattern cascades. In: KDD, pp. 1246–1255 (2014)

    Google Scholar 

  14. Zhao, W.X., Li, S., He, Y., Chang, E.Y., Wen, J.R., Li, X.: Connecting social media to e-commerce: cold-start product recommendation using microblogging information. IEEE Trans. Knowl. Data Eng. 28(5), 1147–1159 (2016)

    Article  Google Scholar 

  15. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Statist. 29, 1189–1232 (2000)

    Article  MathSciNet  Google Scholar 

  16. He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: WWW, pp. 507–517 (2016)

    Google Scholar 

Download references

Acknowledgements

This work is supported by NSF of China (No. 61602237, No. 61672313), 973 Program (No. 2015CB352501), NSF of Shandong, China (No. ZR2017MF065), NSF of Jiangsu, China (No. BK20171420). This work is also supported by US NSF through grants IIS-1526499, and CNS-1626432.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhaohui Peng .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, X., Peng, Z., Wang, S., Yu, P.S., Fu, W., Hong, X. (2018). Cross-Domain Recommendation for Cold-Start Users via Neighborhood Based Feature Mapping. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-91452-7_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91451-0

  • Online ISBN: 978-3-319-91452-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics