Advertisement

On Cross-Domain Transfer in Venue Recommendation

  • Jarana ManotumruksaEmail author
  • Dimitrios Rafailidis
  • Craig Macdonald
  • Iadh Ounis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)

Abstract

Venue recommendation strategies are built upon Collaborative Filtering techniques that rely on Matrix Factorisation (MF), to model users’ preferences. Various cross-domain strategies have been proposed to enhance the effectiveness of MF-based models on a target domain, by transferring knowledge from a source domain. Such cross-domain recommendation strategies often require user overlap, that is common users on the different domains. However, in practice, common users across different domains may not be available. To tackle this problem, recently, several cross-domains strategies without users’ overlaps have been introduced. In this paper, we investigate the performance of state-of-the-art cross-domain recommendation that do not require overlap of users for the venue recommendation task on three large Location-based Social Networks (LBSN) datasets. Moreover, in the context of cross-domain recommendation we extend a state-of-the-art sequential-based deep learning model to boost the recommendation accuracy. Our experimental results demonstrate that state-of-the-art cross-domain recommendation does not clearly contribute to the improvements of venue recommendation systems, and, further we validate this result on the latest sequential deep learning-based venue recommendation approach. Finally, for reproduction purposes we make our implementations publicly available.

Keywords

Cross-domain recommendation Venue suggestion Transfer learning 

References

  1. 1.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)CrossRefGoogle Scholar
  2. 2.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of WSDM (2011)Google Scholar
  3. 3.
    Manotumruksa, J., Macdonald, C., Ounis, I.: Matrix factorisation with word embeddings for rating prediction on location-based social networks. In: Jose, J.M., et al. (eds.) ECIR 2017. LNCS, vol. 10193, pp. 647–654. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-56608-5_61CrossRefGoogle Scholar
  4. 4.
    Manotumruksa, J., Macdonald, C., Ounis, I.: Regularising factorised models for venue recommendation using friends and their comments. In: Proceedings of CIKM (2016)Google Scholar
  5. 5.
    Yuan, F., Guo, G., Jose, J., Chen, L., Yu, H.: Joint geo-spatial preference and pairwise ranking for point-of-interest recommendation. In: Proceedings of ICTAI (2016)Google Scholar
  6. 6.
    Yu, F., Liu, Q., Wu, S., Wang, L., Tan, T.: A dynamic recurrent model for next basket recommendation. In: Proceedings of SIGIR (2016)Google Scholar
  7. 7.
    Tang, S., Wu, Z., Chen, K.: Movie recommendation via BLSTM. In: Amsaleg, L., Guðmundsson, G.Þ., Gurrin, C., Jónsson, B.Þ., Satoh, S. (eds.) MMM 2017. LNCS, vol. 10133, pp. 269–279. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-51814-5_23CrossRefGoogle Scholar
  8. 8.
    Zhang, Y., et al.: Sequential click prediction for sponsored search with recurrent neural networks. In: Proceedings of AAAI (2014)Google Scholar
  9. 9.
    Cheng, C., Yang, H., Lyu, M.R., King, I.: Where you like to go next: successive point-of-interest recommendation. In: Proceedings of IJCAI (2013)Google Scholar
  10. 10.
    Manotumruksa, J., Macdonald, C., Ounis, I.: A deep recurrent collaborative filtering framework for venue recommendation. In: Proceedings of CIKM (2017)Google Scholar
  11. 11.
    Li, B., Yang, Q., Xue, X.: Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction. In: Proceedings of IJCAI (2009)Google Scholar
  12. 12.
    Zang, Y., Hu, X.: LKT-FM: a novel rating pattern transfer model for improving non-overlapping cross-domain collaborative filtering. In: Ceci, M., Hollmén, J., Todorovski, L., Vens, C., Džeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10535, pp. 641–656. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-71246-8_39CrossRefGoogle Scholar
  13. 13.
    Shu, K., Wang, S., Tang, J., Wang, Y., Liu, H.: Crossfire: cross media joint friend and item recommendations. In: Proceedings of WSDM (2018)Google Scholar
  14. 14.
    Farseev, A., Samborskii, I., Filchenkov, A., Chua, T.S.: Cross-domain recommendation via clustering on multi-layer graphs. In: Proceedings of SIGIR (2017)Google Scholar
  15. 15.
    Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., Zhu, C.: Personalized recommendation via cross-domain triadic factorization. In: Proceedings of WWW (2013)Google Scholar
  16. 16.
    Hu, L., Cao, L., Cao, J., Gu, Z., Xu, G., Yang, D.: Learning informative priors from heterogeneous domains to improve recommendation in cold-start user domains (2016)CrossRefGoogle Scholar
  17. 17.
    Mirbakhsh, N., Ling, C.X.: Improving top-n recommendation for cold-start users via cross-domain information. In: Proceedings of TKDD (2015)CrossRefGoogle Scholar
  18. 18.
    He, M., Zhang, J., Yang, P., Yao, K.: Robust transfer learning for cross-domain collaborative filtering using multiple rating patterns approximation. In: Proceedings of WSDM (2018)Google Scholar
  19. 19.
    Coyle, M., Smyth, B.: (Web search)shared: social aspects of a collaborative, community-based search network. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 103–112. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-70987-9_13CrossRefGoogle Scholar
  20. 20.
    Cremonesi, P., Quadrana, M.: Cross-domain recommendations without overlapping data: myth or reality? In: Proceedings of RecSys (2014)Google Scholar
  21. 21.
    Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of SIGKDD (2008)Google Scholar
  22. 22.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of UAI (2009)Google Scholar
  23. 23.
    He, X., Zhang, H., Kan, M.Y., Chua, T.S.: Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of SIGIR (2016)Google Scholar
  24. 24.
    He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of WWW (2017)Google Scholar
  25. 25.
    Xiang, L., et al.: Temporal recommendation on graphs via long-and short-term preference fusion. In: Proceedings of SIGKDD, pp. 723–732. ACM (2010)Google Scholar
  26. 26.
    Lee, S., Song, S.i., Kahng, M., Lee, D., Lee, S.g.: Random walk based entity ranking on graph for multidimensional recommendation. In: Proceedings of RecSys, pp. 93–100. ACM (2011)Google Scholar
  27. 27.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jarana Manotumruksa
    • 1
    Email author
  • Dimitrios Rafailidis
    • 2
  • Craig Macdonald
    • 1
  • Iadh Ounis
    • 1
  1. 1.University of GlasgowGlasgowUK
  2. 2.Maastricht UniversityMaastrichtNetherlands

Personalised recommendations