Abstract
With the increasing popularity of location-aware social media applications, Point of interest (POI) predictions and POI recommendations have been extensively studied. However, most of the existing research is to predict the next POI for a user. In this paper, we consider a new research problem, which is to predict the number of users visiting the POI during a particular time period. In this work, we extend the TSWNN model structure and propose a new method based on Factorization Machine (FM) and Deep Neural Network (DNN) to learn check-in features—TSWNN+. More specifically, this paper uses the Factorization Machine to learn the latent attributes of user check-in and time features. Then, the DNN is used to bridge time features, space features, and weather features to better mine the latent check-in behavior pattern of the user at the POI. In addition, we design a negative instance algorithm to augment training samples. In order to solve the problem of gradient disappearance caused by DNN, the residual structure is adopted. The experimental results on two classical LBSN datasets—Gowalla and Brightkite show the superior performance of the constructed model.
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References
Song, C., Qu, Z., Blumn, N., et al.: Limits of predictability in human mobility. Am. Assoc. Adv. Sci. 327(5968), 1018–1021 (2010)
Tang, L.A., Zheng, Y., Yuan, J., et al.: A framework of traveling companion discovery on trajectory data streams. ACM Trans. Intell. Syst. Technol. 5(1), 1–34 (2014)
Noulas, A., Scellato, S., Lathia, N., et al.: Mining user mobility features for next place prediction in location-based services. In: Twelfth IEEE International Conference on Data Mining, vol. 5, no. 1, pp. 1038–1043 (2013)
Su, C., Peng, S.W., Xie, X.Z., et al.: Check-in prediction based on deep learning and factorization machine. In: Computer Science , vol. 46, no. 5, pp. 185–190 (2019)
He, J., Li, X., Liao, L., et al.: Inferring a personalized next point-of-interest recommendation model with latent behavior patterns. In: Thirtieth AAAI Conference on Artificial Intelligence AAAI Press, pp. 137–143 (2016)
Zheng, V.W., Cao, B., Zheng, Y., et al.: Collaborative filtering meets mobile recommendation: a user-centered approach. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI), pp. 236–241 (2010)
Ye, M., Yin, P., Lee, W.C., et al.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 325–334 (2010)
Ye, J., Zhu, Z., Cheng, H.: What’s your next move: user activity prediction in location-based social networks. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp. 171–179 (2013)
Gao, H., Tang, J., Liu, H.: gSCorr: modeling geo-social correlations for new check-ins on location-based social networks. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM), pp. 1582–1586 (2012)
Cao, J., Xu, S., Zhu, X., et al.: Effective fine-grained location prediction based on user check-in pattern in LBSNs. J. Netw. Comput. Appl. 108, 64–75 (2018)
Chang, J., Sun, E.: Location3: how users share and respond to location-based data on social. In: International Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, pp. 74–80. DBLP, July 2011
Long, V., Nguyen, P., Nahrstedt, K., et al.: Characterizing and modeling people movement from mobile phone sensing traces. Pervasive Mob. Comput. 17, 220–235 (2015)
Do, T.M.T., Gatica-Perez, D.: Where and what: using smart phones to predict next locations and applications in daily life. Pervasive Mob. Comput. 12, 79–91 (2014)
Liu, Q., Wu, S., Wang, L., et al.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: Thirtieth AAAI Conference on Artificial Intelligence AAAI Press, pp. 194–200 (2016)
Al-Molegi, A., Jabreel, M., Ghaleb, B.: STF-RNN: space time features-based recurrent neural network for predicting people next location. In: IEEE Symposium Series on Computational Intelligence, pp. 1–7 (2016)
Zeiler, M.D., Taylor, G.W., Fergus, R.: Adaptive deconvolutional networks for mid and high level feature learning. In: International Conference on Computer Vision IEEE Computer Society, pp. 2018–2025 (2011)
Li, D., Abdelhamid, O., Yu, D.: A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion. In: IEEE International Conference on Acoustics, pp. 6669–6673 (2013)
She, Y., He, X., Gao, J., et al.: A latent semantic model with convolutional-pooling structure for information retrieval, pp. 101–110. ACM (2014)
Guo, H., Tang, R., Ye, Y., et al.: DeepFM: a factorization-machine based neural network for CTR prediction. In: The Twenty-Sixth International Joint Conference on Artificial Intelligence, vol. 1703, pp. 1725–1731 (2017)
Rendle, S.: Factorization machines. In: IEEE International Conference on Data Mining, pp. 995–1000 (2010)
He, X.N., Liao, L.Z., Zhang, H.W., et al.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2015)
Zheng, Y., Tang, B., Ding, W., et al.: A neural autoregressive approach to collaborative filtering. In: Proceedings of the Thirty-Third International Conference on Machine Learning, pp. 764–773 (2016)
Acknowledgments
This work was supported by the National Nature Science Foundation of China (grant numbers 61271259), the Chongqing Nature Science Foundation (grant numbers CSTC2016jcyjA0398, CTSC2012jjA40038), and the Research Project of Chongqing Education Commission (grant numbers KJ120501C).
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Su, C., Liu, N., Xie, X., Peng, S. (2019). TSWNN+: Check-in Prediction Based on Deep Learning and Factorization Machine. In: Younas, M., Awan, I., Benbernou, S. (eds) Big Data Innovations and Applications. Innovate-Data 2019. Communications in Computer and Information Science, vol 1054. Springer, Cham. https://doi.org/10.1007/978-3-030-27355-2_5
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DOI: https://doi.org/10.1007/978-3-030-27355-2_5
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