Advertisement

Conclusion and Future Work

Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

This chapter summarizes the main contributions of this monograph and provides several interesting future directions.

Keywords

Ranking method Online recommendation Deep learning 

References

  1. 1.
    Bao, J., Zheng, Y., Mokbel, M.F.: Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 199–208. ACM (2012)Google Scholar
  2. 2.
    Bubeck, S., Cesa-Bianchi, N., et al.: Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends \(\textregistered \)in Machine Learning 5(1), 1–122 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cao, Z., Qin, T., Liu, T.Y., Tsai, M.F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th international conference on Machine learning, pp. 129–136. ACM (2007)Google Scholar
  4. 4.
    Cheng, C., Yang, H., King, I., Lyu, M.R.: Fused matrix factorization with geographical and social influence in location-based social networks. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, pp. 17–23. AAAI Press (2012)Google Scholar
  5. 5.
    Cheng, C., Yang, H., King, I., Lyu, M.R.: A unified point-of-interest recommendation framework in location-based social networks. ACM Trans. Intell. Syst. Technol. (TIST) 8(1), 10 (2016)Google Scholar
  6. 6.
    Feng, S., Cong, G., An, B., Chee, Y.M.: POI2Vec: Geographical latent representation for predicting future visitors. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA., pp. 102–108 (2017)Google Scholar
  7. 7.
    Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y.M., Yuan, Q.: Personalized ranking metric embedding for next new poi recommendation. In: Proceedings of the 24th International Conference on Artificial Intelligence, pp. 2069–2075. AAAI Press (2015)Google Scholar
  8. 8.
    Gao, H., Tang, J., Hu, X., Liu, H.: Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 93–100. ACM (2013)Google Scholar
  9. 9.
    Gao, H., Tang, J., Hu, X., Liu, H.: Content-aware point-of-interest recommendation on location-based social networks. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 1721–1727. AAAI Press (2015)Google Scholar
  10. 10.
    He, J., Li, X., Liao, L.: Category-aware next point-of-interest recommendation via Listwise Bayesian personalized ranking. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 1837–1843. AAAI Press (2017)Google Scholar
  11. 11.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  12. 12.
    Lee, J., Bengio, S., Kim, S., Lebanon, G., Singer, Y.: Local collaborative ranking. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 85–96. ACM (2014)Google Scholar
  13. 13.
    Li, X., Cong, G., Li, X.L., Pham, T.A.N., Krishnaswamy, S.: Rank-GeoFM: a ranking based geographical factorization method for point-of-interest recommendation. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 433–442. ACM (2015)Google Scholar
  14. 14.
    Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 194–200 (2016)Google Scholar
  15. 15.
    Liu, T.Y.: Learning to rank for information retrieval. Found. Trends Inf. Retr. 3(3), 225–331 (2009)CrossRefGoogle Scholar
  16. 16.
    Liu, X., Liu, Y., Li, X.: Exploring the context of locations for personalized location recommendations. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 1188–1194. AAAI Press (2016)Google Scholar
  17. 17.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  18. 18.
    Qin, L., Chen, S., Zhu, X.: Contextual combinatorial bandit and its application on diversified online recommendation. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 461–469. SIAM (2014)CrossRefGoogle Scholar
  19. 19.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)Google Scholar
  20. 20.
    Usunier, N., Buffoni, D., Gallinari, P.: Ranking with ordered weighted pairwise classification. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1057–1064. ACM (2009)Google Scholar
  21. 21.
    Weston, J., Bengio, S., Usunier, N.: Large scale image annotation: learning to rank with joint word-image embeddings. Mach. Learn. 81(1), 21–35 (2010)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., Wang, S.: Learning graph-based POI embedding for location-based recommendation. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 15–24. ACM (2016)Google Scholar
  23. 23.
    Yin, H., Sun, Y., Cui, B., Hu, Z., Chen, L.: Lcars: a location-content-aware recommender system. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 221–229. ACM (2013)Google Scholar
  24. 24.
    Zhao, S., Zhao, T., King, I., Lyu, M.R.: Geo-Teaser: geo-temporal sequential embedding rank for point-of-interest recommendation. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 153–162. International World Wide Web Conferences Steering Committee (2017)Google Scholar
  25. 25.
    Zhao, S., Zhao, T., Yang, H., Lyu, M.R., King, I.: STELLAR: spatial-temporal latent ranking for successive point-of-interest recommendation. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 315–322 (2016)Google Scholar
  26. 26.
    Zhao, T., King, I.: Constructing reliable gradient exploration for online learning to rank. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 1643–1652. ACM (2016)Google Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Youtu LabTencentShenzhenChina
  2. 2.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong KongChina

Personalised recommendations