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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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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