Abstract
Point-of-interest (POI) recommendation is an important part of recommendation systems in location-based social networks. Most existing POI recommendation systems, such as collaborative filtering based and context-aware methods, usually use hand-designed or manually selected features to achieve the recommendation. However, the information in the location-based social networks has very complicated relationships with each other, e.g., the latent relationships among users, POIs and user preferences, thus leading to poor recommendation accuracy. We propose a two-stage method to address this problem. In the first stage, user and POI profiles are abstracted using statistical methods. Then in the second stage, a deep neural network (DNN) is used to predict ratings on these candidate POIs, and finally the topN list of POIs is obtained. Experimental results on the Gowalla and Brightkite dataset show the effectiveness of our DNN based recommendation method.
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References
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, Beijing, China, pp. 325–334. ACM (2011)
Cheng, C., Yang, H., King, I., et al.: Fused matrix factorization with geographical and social influence in location-based social networks. In: Twenty-Sixth AAAI Conference on Artificial Intelligence, Toronto, Ontario, Canada, pp. 17–23. AAAI (2012)
Yuan, Q., Cong, G., Ma, Z., et al.: Time-aware point-of-interest recommendation. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, pp. 363–372. ACM (2013)
Li, H., Ge, Y., Hong, R., et al.: Point-of-interest recommendations: learning potential check-ins from friends. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, pp. 975–984. ACM (2016)
Gao, H., Tang, J., Hu, X., et al.: Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of the 7th ACM Conference on Recommender Systems, Hong Kong, China, pp. 93–100. ACM (2013)
Zhang, J.-D., Chow, C.-Y.: GeoSoCa: exploiting geographical, social and categorical correlations for point-of-interest recommendations. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, pp. 443–452. ACM (2015)
Lian, D., Ge, Y., Zhang, F., et al.: Content-aware collaborative filtering for location recommendation based on human mobility data. In: Proceedings of the 15th IEEE International Conference on Data Mining, Atlantic City, New Jersey, USA, pp. 261–270. IEEE (2015)
van den Oord, A., Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA, pp. 2643–2651. Neural Information Processing Systems Foundation (NIPS) (2013)
Covington, P., Adams, J., Sargin, E.: Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, Boston, Massachusetts, USA, pp. 191–198. ACM Press (2016)
Cheng, H.-T., Ispir, M., Anil, R., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, Boston, MA, USA, pp. 7–10. ACM Press (2016)
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California, USA, pp. 1082–1090. ACM Press (2011)
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Wang, Y., Zhong, Z., Yang, A., Jing, N. (2018). A Deep Point-of-Interest Recommendation System in Location-Based Social Networks. In: Tan, Y., Shi, Y., Tang, Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science(), vol 10943. Springer, Cham. https://doi.org/10.1007/978-3-319-93803-5_51
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DOI: https://doi.org/10.1007/978-3-319-93803-5_51
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