RecNet: a deep neural network for personalized POI recommendation in location-based social networks

References

• Bao, J., et al. 2015. Recommendations in location-based social networks: a survey. GeoInformatica, 19 (3), 525–565. doi:10.1007/s10707-014-0220-8
   Web of Science ®Google Scholar
• Cheng, C., et al., 2012. Fused matrix factorization with geographical and social influence in location-based social networks. In: Proceedings of the 26th AAAI, Toronto, ON. AAAI, 17–23.
   Google Scholar
• Cheng, C., et al., 2013. Where you like to go next: successive point-of-interest recommendation. In: Proceedings of the 23th IJCAI, Beijing. Morgan Kaufmann, 2605–2611.
   Google Scholar
• Cheng, H.T., et al., 2016. Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS 2016. New York, NY, USA: ACM, 7–10.
   Google Scholar
• Covington, P., Adams, J., and Sargin, E., 2016. Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM RecSys, Boston, MA. ACM, 191–198.
   Google Scholar
• Feng, S., et al., 2015. Personalized ranking metric embedding for next new poi recommendation. In: Proceedings of the 25th IJCAI, Buenos Aires. Morgan Kaufmann, 2069–2075.
   Google Scholar
• Feng, S., et al., 2017. POI2Vec: geographical latent representation for predicting future visitors. In: Proceedings of the 31th AAAI, San Francisco, CA. AAAI, 102–108.
   Google Scholar
• Glorot, X., Bordes, A., and Bengio, Y. 2011, Deep sparse rectifier neural networks. In: Proceedings of the 14th AISTATS, Fort Lauderdale, FL. JMLR, 315–323.
   Google Scholar
• He, X., et al., 2017. Neural collaborative filtering. In: Proceedings of the 26th WWW, WWW ’17. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee, 173–182.
   Google Scholar
• Hu, Y., Koren, Y., and Volinsky, C., 2008. Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th ICDM, Pisa. IEEE, 263–272.
   Google Scholar
• Ioffe, S. and Szegedy, C., 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd ICML, Lille. ACM, 448–456.
   Google Scholar
• Kingma, D.P. and Ba, J.L., 2015. Adam: a method for stochastic optimization. In: Proceedings of ICLR, San Diego, CA, 1–15.
   Google Scholar
• Krizhevsky, A., Sutskever, I., and Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. In: Proceedings of the 26th NIPS, Lake Tahoe, NV. MIT Press, 1097–1105.
   Google Scholar
• Kuang, D., Ding, C., and Park, H., 2012. Symmetric nonnegative matrix factorization for graph clustering. In: Proceedings of SDM. SIAM, Anaheim, CA. 106–117.
   Google Scholar
• LeCun, Y., Bengio, Y., and Hinton, G., 2015. Deep learning. Nature, 521 (7553), 436–444. doi:10.1038/nature14539
   PubMed Web of Science ®Google Scholar
• Li, H., et al., 2016. Point-of-interest recommendations: learning potential check-ins from friends. In: Proceedings of the 22th ACM SIGKDD, San Francisco, CA. ACM, 975–984.
   Google Scholar
• Li, X., et al., 2015. Rank-GeoFM: a ranking based geographical factorization method for point of interest recommendation. In: Proceedings of the 38th ACM SIGIR, Santiago. ACM, 433–442.
   Google Scholar
• Lian, D., et al., 2014. GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: Proceedings of the 20th ACM SIGKDD, New York City. ACM, 831–840.
   Google Scholar
• Liang, D., et al., 2016. Factorization meets the item embedding: regularizing matrix factorization with item co-occurrence. In: Proceedings of the 10th ACM RecSys, 59–66.
   Google Scholar
• Liu, X., et al., 2013. Personalized point-of-interest recommendation by mining users’ preference transition. In: Proceedings of the 22nd ACM CIKM, San Francisco, CA. ACM, 733–738.
   Google Scholar
• Liu, Y., et al., 2014. Exploiting geographical neighborhood characteristics for location recommendation. In: Proceedings of the 23rd CIKM. ACM, Shanghai. ACM, 739–748.
   Google Scholar
• Liu, Y., et al. 2017. An experimental evaluation of point-of-interest recommendation in location-based social networks. Proceedings VLDB Endow, 10 (10), 1010–1021. doi:10.14778/3115404.3115407
   Web of Science ®Google Scholar
• Mikolov, T., et al., 2013. Distributed representations of words and phrases and their compositionality. In: Proceedings of the 27th NIPS, Lake Tahoe, NV. MIT Press, 3111–3119.
   Google Scholar
• Pham, T.A.N., Li, X., and Cong, G., 2017. A general model for out-of-town region recommendation. In: Proceedings of the 26th WWW. International World Wide Web Conferences Steering Committee, Perth, 401–410.
   Google Scholar
• Rendle, S., et al., 2009. Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th UAI, AUAI Press, Montreal, QC. AUAI, 452–461.
   Google Scholar
• Srivastava, N., et al., 2014. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15 (1), 1929–1958.
   Google Scholar
• Wang, S., et al., 2017. What your images reveal: exploiting visual contents for point-of-interest recommendation. In: Proceedings of the 26th WWW. International World Wide Web Conferences Steering Committee, Perth, 391–400.
   Google Scholar
• Weston, J., Bengio, S., and Usunier, N., 2010. Large scale image annotation: learning to rank with joint word-image embeddings. Machine Learning, 81 (1), 21–35. doi:10.1007/s10994-010-5198-3
   Web of Science ®Google Scholar
• Xie, M., et al., 2016. Learning graph-based poi embedding for location-based recommendation. In: Proceedings of the 25th ACM CIKM, Indianapolis, IN. ACM, 15–24.
   Google Scholar
• Yang, D., et al. 2014. Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Transactions on Systems Man and Cybernetics Systems, 45 (1), 129–142. doi:10.1109/TSMC.2014.2327053
   Web of Science ®Google Scholar
• Ye, M., et al., 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th ACM SIGIR, Beijing. ACM, 325–334.
   Google Scholar
• Yin, H., et al., 2013. LCARS: a location-content-aware recommender system. In: Proceedings of the 19th ACM SIGKDD, Chicago, IL. ACM, 221–229.
   Google Scholar
• Yuan, Q., et al., 2013. Time-aware point-of-interest recommendation. In: Proceedings of the 36th ACM SIGIR, Dublin. ACM, 363–372.
   Google Scholar
• Zhang, C. and Wang, K., 2016. POI recommendation through cross-region collaborative filtering. Knowledge and Information Systems, 46 (2), 369–387. doi:10.1007/s10115-015-0825-8
   Web of Science ®Google Scholar
• Zhao, S., et al., 2017. Geo-teaser: geo-temporal sequential embedding rank for point-of-interest recommendation. In: Proceedings of the 26th WWW, Perth. ACM, 153–162.
   Google Scholar