Advertisement

GeoDCF: Deep Collaborative Filtering with Multifaceted Contextual Information in Location-Based Social Networks

  • Dimitrios RafailidisEmail author
  • Fabio Crestani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11052)

Abstract

In this study we investigate the recommendation problem with multifaceted contextual information to overcome the scarcity of users’ check-in data in Location-based Social Networks. To generate accurate personalized Point-of-Interest (POI) recommendations in the presence of data scarcity, we account for both users’ and POIs’ contextual information such as the social influence of friends, as well as the geographical and sequential transition influence of POIs on user’s check-in behavior. We first propose a multi-view learning strategy to capture the multifaceted contextual information of users and POIs along with users’ check-in data. Then, we feed the learned user and POI latent vectors to a deep neural framework, to capture their non-linear correlations. Finally, we formulate the objective function of our geo-based deep collaborative filtering model (GeoDCF) as a Bayesian personalized ranking problem to focus on the top-k recommendation task and we learn the parameters of our model via backpropagation. Our experiments on real-world datasets confirm that GeoDCF achieves high recommendation accuracy, significantly outperforming other state-of-the-art methods. Furthermore, we confirm the influence of both users’ and POIs’ contextual information on our GeoDCF model. The evaluation datasets are publicly available at: http://snap.stanford.edu/data/loc-gowalla.html, https://sites.google.com/site/yangdingqi/home/foursquare-dataset.

Keywords

Point-of-interest recommendation Deep collaborative filtering Multifaceted contextual information Location-Based Social Networks 

References

  1. 1.
    Cheng, C., Yang, H., Lyu, M.R., King, I.: Where you like to go next: successive point-of-interest recommendation. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 2605–2611 (2013)Google Scholar
  2. 2.
    Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining, pp. 1082–1090 (2011)Google Scholar
  3. 3.
    Ding, D., Zhang, M., Li, S., Tang, J., Chen, X., Zhou, Z.: BayDNN: friend recommendation with Bayesian personalized ranking deep neural network. In: Proceedings of the ACM International Conference on Information and Knowledge Management, pp. 1479–1488 (2017)Google Scholar
  4. 4.
    Farseev, A., Samborskii, I., Filchenkov, A., Chua, T.: Cross-domain recommendation via clustering on multi-layer graphs. In: Proceedings of the ACM International Conference on Research and Development in Information Retrieval, pp. 195–204 (2017)Google Scholar
  5. 5.
    Gao, J., Han, J., Liu, J., Wang, C.: Multi-view clustering via joint nonnegative matrix factorization. In: Proceedings of the SIAM International Conference on Data Mining, pp. 252–260 (2013)Google Scholar
  6. 6.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the IEEE International Conference on Data Mining, pp. 263–272 (2008)Google Scholar
  7. 7.
    Li, H., Ge, Y., Hong, R., Zhu, H.: Point-of-interest recommendations: learning potential check-ins from friends. In: Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining, pp. 975–984 (2016)Google Scholar
  8. 8.
    Li, S., Kawale, J., Fu, Y.: Deep collaborative filtering via marginalized denoising auto-encoder. In: Proceedings of the ACM International Conference on Information and Knowledge Management, pp. 811–820 (2015)Google Scholar
  9. 9.
    Li, X., Cong, G., Li, X., Pham, T.N., Krishnaswamy, S.: Rank-GeoFM: a ranking based geographical factorization method for point of interest recommendation. In: Proceedings of the ACM International Conference on Research and Development in Information Retrieval, pp. 433–442 (2015)Google Scholar
  10. 10.
    Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., Rui, Y.: GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining, pp. 831–840 (2014)Google Scholar
  11. 11.
    Liu, Y., Wei, W., Sun, A., Miao, C.: Exploiting geographical neighborhood characteristics for location recommendation. In: Proceedings of the ACM International Conference on Information and Knowledge Management, pp. 739–748 (2014)Google Scholar
  12. 12.
    Manotumruksa, J., Macdonald, C., Ounis, I.: A personalised ranking framework with multiple sampling criteria for venue recommendation. In: Proceedings of the ACM International Conference on Information and Knowledge Management, pp. 1469–1478 (2017)Google Scholar
  13. 13.
    Rafailidis, D., Crestani, F.: Collaborative ranking with social relationships for top-n recommendations. In: Proceedings of the ACM International Conference on Research and Development in Information Retrieval, pp. 785–788 (2016)Google Scholar
  14. 14.
    Rafailidis, D., Crestani, F.: Joint collaborative ranking with social relationships in top-n recommendation. In: Proceedings of the ACM International Conference on Information and Knowledge Management, pp. 1393–1402 (2016)Google Scholar
  15. 15.
    Rafailidis, D., Crestani, F.: Top-n recommendation via joint cross-domain user clustering and similarity learning. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9852, pp. 426–441. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46227-1_27CrossRefGoogle Scholar
  16. 16.
    Rafailidis, D., Crestani, F.: A collaborative ranking model for cross-domain recommendations. In: Proceedings of the ACM International Conference on Information and Knowledge Management, pp. 2263–2266 (2017)Google Scholar
  17. 17.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the International Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)Google Scholar
  18. 18.
    Wang, H., Wang, N., Yeung, D.: Collaborative deep learning for recommender systems. In: Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining, pp. 1235–1244 (2015)Google Scholar
  19. 19.
    Xu, L., Choy, C., Li, Y.: Deep sparse rectifier neural networks for speech denoising. In: Proceedings of the IEEE International Workshop on Acoustic Signal Enhancement, pp. 1–5 (2016)Google Scholar
  20. 20.
    Yang, C., Bai, L., Zhang, C., Yuan, Q., Han, J.: Bridging collaborative filtering and semi-supervised learning: a neural approach for POI recommendation. In: Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining, pp. 1245–1254 (2017)Google Scholar
  21. 21.
    Ye, M., Yin, P., Lee, W., Lee, D.L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceeding of the ACM International Conference on Research and Development in Information Retrieval, pp. 325–334 (2011)Google Scholar
  22. 22.
    Ying, H., Chen, L., Xiong, Y., Wu, J.: Collaborative deep ranking: a hybrid pair-wise recommendation algorithm with implicit feedback. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, R. (eds.) PAKDD 2016. LNCS (LNAI), vol. 9652, pp. 555–567. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-31750-2_44CrossRefGoogle Scholar
  23. 23.
    Yuan, F., Jose, J.M., Guo, G., Chen, L., Yu, H., Alkhawaldeh, R.S.: Joint geo-spatial preference and pairwise ranking for point-of-interest recommendation. In: Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, pp. 46–53 (2016)Google Scholar
  24. 24.
    Zhang, J., Chow, C.: iGSLR: personalized geo-social location recommendation: a kernel density estimation approach. In: Proceeding of the ACM International Conference on Advances in Geographic Information Systems, pp. 324–333 (2013)Google Scholar
  25. 25.
    Zhang, J., Chow, C.: GeoSoCa: exploiting geographical, social and categorical correlations for point-of-interest recommendations. In: Proceedings of the ACM International Conference on Research and Development in Information Retrieval, pp. 443–452 (2015)Google Scholar
  26. 26.
    Zhang, J., Chow, C., Li, Y.: LORE: exploiting sequential influence for location recommendations. In: Proceedings of the ACM International Conference on Advances in Geographic Information Systems, pp. 103–112 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MonsMonsBelgium
  2. 2.Department of Data Science and Knowledge EngineeringMaastricht UniversityMaastrichtThe Netherlands
  3. 3.Faculty of InformaticsUniversità della Svizzera italianaLuganoSwitzerland

Personalised recommendations