Abstract
Point-of-interest (POI) prediction is a key task in location-based social networks. It captures the user preference to predict POIs. Recent studies demonstrate that spatial influence is significant for prediction. The distance can be converted to a weight reflecting the relevance of two POIs or can be utilized to find nearby locations. However, previous studies almost ignore the correlation between user and distance. When people choose the next POI, they will consider the distance at the same time. Besides, spatial influence varies greatly for different users. In this work, we propose a Distance-to-Preference (Distance2Pre) network for the next POI prediction. We first acquire the user’s sequential preference by modeling check-in sequences. Then, we propose to acquire the spatial preference by modeling distances between successive POIs. This is a personalized process and can capture the relationship in user-distance interactions. Moreover, we propose two preference encoders which are a linear fusion and a non-linear fusion. Such encoders explore different ways to fuse the above two preferences. Experiments on two real-world datasets show the superiority of our proposed network.
Q. Cui and Y. Tang—These authors contributed equally to this paper.
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References
Cheng, C., Yang, H., Lyu, M.R., King, I.: Where you like to go next: successive point-of-interest recommendation. In: IJCAI, vol. 13, pp. 2605–2611 (2013)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP, pp. 1724–1734 (2014)
Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., Song, L.: Recurrent marked temporal point processes: embedding event history to vector. In: SIGKDD, pp. 1555–1564. ACM (2016)
Feng, S., Cong, G., An, B., Chee, Y.M.: POI2Vec: geographical latent representation for predicting future visitors. In: AAAI, pp. 102–108 (2017)
Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y.M., Yuan, Q.: Personalized ranking metric embedding for next new POI recommendation. In: IJCAI, pp. 2069–2075 (2015)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173–182 (2017)
He, X., Zhang, H., Kan, M.Y., Chua, T.S.: Fast matrix factorization for online recommendation with implicit feedback. In: SIGIR, pp. 549–558. ACM (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Jing, H., Smola, A.J.: Neural survival recommender. In: WSDM, pp. 515–524. ACM (2017)
Kapoor, K., Sun, M., Srivastava, J., Ye, T.: A hazard based approach to user return time prediction. In: SIGKDD, pp. 1719–1728. ACM (2014)
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: SIGIR, pp. 433–442. ACM (2015)
Liu, Q., Wu, S., Wang, D., Li, Z., Wang, L.: Context-aware sequential recommendation. In: ICDM, pp. 1053–1058 (2016)
Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: AAAI, pp. 194–200 (2016)
Liu, X., Liu, Y., Li, X.: Exploring the context of locations for personalized location recommendations. In: IJCAI, pp. 1188–1194 (2016)
Mnih, V., Heess, N., Graves, A., et al.: Recurrent models of visual attention. In: NIPS, pp. 2204–2212 (2014)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461 (2009)
Wang, H., Shen, H., Ouyang, W., Cheng, X.: Exploiting poi-specific geographical influence for point-of-interest recommendation. In: IJCAI, pp. 3877–3883 (2018)
Wang, S., Hu, L., Cao, L., Huang, X., Lian, D., Liu, W.: Attention-based transactional context embedding for next-item recommendation. In: AAAI, pp. 2532–2539 (2018)
Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N.M.: Time-aware point-of-interest recommendation. In: SIGIR, pp. 363–372. ACM (2013)
Zhao, S., Zhao, T., King, I., Lyu, M.R.: Geo-teaser: geo-temporal sequential embedding rank for point-of-interest recommendation. In: WWW, pp. 153–162 (2017)
Acknowledgment
This work is jointly supported by National Natural Science Foundation of China (61772528), National Key Research and Development Program (2016YFB1001000), National Natural Science Foundation of China (U1435221).
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Cui, Q., Tang, Y., Wu, S., Wang, L. (2019). Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_23
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DOI: https://doi.org/10.1007/978-3-030-16142-2_23
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