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Attentive sequential model based on graph neural network for next poi recommendation


With the rapid development of Information Technology, there exist massive amounts of data available on the Internet, which result in a severe information overload problem. Especially, it becomes more and more challenging but necessary to help users find the contents or services that they really need. To address the problem mentioned above, recommender systems have been developed to exploit user’s historical behavior data and provide personalized services for promoting customer experiences in many fields, such as Point of Interest (POI) applications, multimedia services, and e-commerce websites. Specifically, in POI recommendation, user’s next check-in behaviors depend on both long- and short-term preferences. However, traditional recommendation methods often ignore the dynamic changes of user’s short-term preferences over time, which limits their performance. Besides, many existing methods cannot fully exploit the complex correlations and transitions between POI in check-ins sequences. In this paper, we propose an A ttentive S equential model based on G raph N eural N etwork (ASGNN) for accurate next POI recommendation. Specifically, ASGNN firstly models user’s check-in sequences as graphs and then use Graph Neural Networks (GNN) to learn the informative low-dimension latent feature vectors (embeddings) of POIs. Secondly, a personalized hierarchical attention network is adopted to exploit complex correlations between users and POIs in check-in sequences and capture user’s long- and short-term preferences. Finally, we perform the next POI recommendation via leveraging user’s long- and short-term preferences obtained from their behavior sequences with ASGNN. Extensive experiments are conducted on three real-world check-in datasets, and the results demonstrate that the proposed model ASGNN outperforms baselines, including some state-of-the-art methods.

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  1. 1.

  2. 2.

  3. 3.

  4. 4.


  1. 1.

    Albadvi, A., Shahbazi, M.: A hybrid recommendation technique based on product category attributes. Expert Syst. Appl. 36(9), 11,480–11,488 (2009)

  2. 2.

    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 (2014)

  3. 3.

    Bansal, T., Belanger, D., McCallum, A.: Ask the gru: Multi-task learning for deep text recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 107–114 (2016)

  4. 4.

    Berg, R.v.d., Kipf, T.N., Welling, M.: Graph convolutional matrix completion. arXiv:1706.02263 (2017)

  5. 5.

    Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv:1406.1078 (2014)

  6. 6.

    Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems, pp. 191–198 (2016)

  7. 7.

    Deng, S., Wang, D., Li, Y., Cao, B., Yin, J., Wu, Z., Zhou, M.: A recommendation system to facilitate business process modeling. IEEE Trans. Cybern. 47(6), 1380–1394 (2016)

    Article  Google Scholar 

  8. 8.

    Elkahky, A.M., Song, Y., He, X.: A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th International Conference on World Wide Web, pp. 278–288 (2015)

  9. 9.

    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)

    Article  Google Scholar 

  10. 10.

    He, R., McAuley, J.: Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation. In: 2016 IEEE 16Th International Conference on Data Mining (ICDM), pp. 191–200. IEEE (2016)

  11. 11.

    Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv:1511.06939(2015)

  12. 12.

    Hsieh, C.K., Yang, L., Cui, Y., Lin, T.Y., Belongie, S., Estrin, D.: Collaborative metric learning. In: Proceedings of the 26th international conference on world wide Web, pp. 193–201 (2017)

  13. 13.

    Huang, L., Ma, Y., Wang, S., Liu, Y.: An attention-based spatiotemporal lstm network for next poi recommendation. IEEE Transactions on Services Computing (2019)

  14. 14.

    Jannach, D., Ludewig, M.: When recurrent neural networks meet the neighborhood for session-based recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 306–310 (2017)

  15. 15.

    Jia, Z., Yang, Y., Gao, W., Chen, X.: User-Based Collaborative Filtering for Tourist Attraction Recommendations. In: 2015 IEEE International Conference on Computational Intelligence & Communication Technology, pp. 22–25. IEEE (2015)

  16. 16.

    Jiang, M., Fang, Y., Xie, H., Chong, J., Meng, M.: User click prediction for personalized job recommendation. World Wide Web 22(1), 325–345 (2019)

    Article  Google Scholar 

  17. 17.

    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv:1412.6980 (2014)

  18. 18.

    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  19. 19.

    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  20. 20.

    Lekakos, G., Caravelas, P.: A hybrid approach for movie recommendation. Multimed. Tools Appl. 36(1-2), 55–70 (2008)

    Article  Google Scholar 

  21. 21.

    Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. arXiv:1511.05493 (2015)

  22. 22.

    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 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 831–840 (2014)

  23. 23.

    Linden, G., Smith, B., York, J.: Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

    Article  Google Scholar 

  24. 24.

    Ma, C., Zhang, Y., Wang, Q., Liu, X.: Point-of-interest recommendation: Exploiting self-attentive autoencoders with neighbor-aware influence. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 697–706 (2018)

  25. 25.

    Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11) (2008)

  26. 26.

    Mnih, V., Heess, N., Graves, A., et al.: Recurrent Models of Visual Attention. In: Advances in Neural Information Processing Systems, pp. 2204–2212 (2014)

  27. 27.

    Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. In: The Adaptive Web, pp. 325–341. Springer (2007)

  28. 28.

    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618 (2012)

  29. 29.

    Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on World wide web, pp. 811–820 (2010)

  30. 30.

    Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  31. 31.

    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)

    Article  Google Scholar 

  32. 32.

    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp. 285–295 (2001)

  33. 33.

    Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2008)

    Article  Google Scholar 

  34. 34.

    Song, W., Xiao, Z., Wang, Y., Charlin, L., Zhang, M., Tang, J.: Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 555–563 (2019)

  35. 35.

    Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 565–573 (2018)

  36. 36.

    Wang, D., Deng, S., Zhang, X., Xu, G.: Learning to embed music and metadata for context-aware music recommendation. World Wide Web 21 (5), 1399–1423 (2018)

    Article  Google Scholar 

  37. 37.

    Wang, P., Guo, J., Lan, Y., Xu, J., Wan, S., Cheng, X.: Learning hierarchical representation model for nextbasket recommendation. In: Proceedings of the 38th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 403–412 (2015)

  38. 38.

    Wang, X., He, X., Cao, Y., Liu, M., Chua, T.S.: Kgat: Knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 950–958 (2019)

  39. 39.

    Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval, pp. 165–174 (2019)

  40. 40.

    Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 346–353 (2019)

  41. 41.

    Yin, H., Wang, W., Wang, H., Chen, L., Zhou, X.: Spatial-aware hierarchical collaborative deep learning for poi recommendation. IEEE Trans. Knowl. Data Eng. 29(11), 2537–2551 (2017)

    Article  Google Scholar 

  42. 42.

    Ying, H., Zhuang, F., Zhang, F., Liu, Y., Xu, G., Xie, X., Xiong, H., Wu, J.: Sequential Recommender System Based on Hierarchical Attention Network. In: IJCAI International Joint Conference on Artificial Intelligence (2018)

  43. 43.

    Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 974–983 (2018)

  44. 44.

    Yu, D., Wanyan, W., Wang, D.: Leveraging contextual influence and user preferences for point-of-interest recommendation. Multimed. Tools Appl., 1–15 (2020)

  45. 45.

    Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.V.: Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 793–803 (2019)

  46. 46.

    Zhao, P., Zhu, H., Liu, Y., Li, Z., Xu, J., Sheng, V.S.: Where to go next: A spatio-temporal lstm model for next poi recommendation. arXiv:1806.06671 (2018)

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Correspondence to Dongjin Yu.

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This research was supported by Natural Science Foundation of Zhejiang Province under No.LQ20F020015, the Fundamental Research Funds for the Provincial University of Zhejiang by Hangzhou Dianzi University under No.GK199900299012-017, and Zhejiang Provincial Key Science and Technology Program Foundation under No.2020C01165.

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Wang, D., Wang, X., Xiang, Z. et al. Attentive sequential model based on graph neural network for next poi recommendation. World Wide Web 24, 2161–2184 (2021).

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  • Recommender system
  • Sequential recommendation
  • POI recommendation
  • Graph neural network
  • Attention