Predicting your next stop-over from location-based social network data with recurrent neural networks
作者： Elena Maria Baralis , Raphaël Troncy , Giuseppe Rizzo , Enrico Palumbo
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摘要: In the past years, Location-based Social Network (LBSN) data have strongly fostered a data-driven approach to recommendation of Points Interest (POIs) in tourism domain. However, an important aspect that is often not taken into account by current approaches temporal correlations among POI categories tourist paths. this work, we collect from Foursquare, extract timed paths sequences temporally neighboring check-ins and use Recurrent Neural (RNN) learn generate new training it predict observed As further step, cluster considering users' demographics separate models for each category users. The evaluation shows e�ectiveness proposed predicting terms model perplexity on test set
参考文章(28)
Ilya Sutskever, Geoffrey E. Hinton, James Martens, Generating Text with Recurrent Neural Networks international conference on machine learning. pp. 1017- 1024 ,(2011)
Tom Gross, Supporting Informed Negotiation Processes in Group Recommender Systems international conference on optoelectronics and microelectronics. ,vol. 14, pp. 53- 61 ,(2015) , 10.1515/ICOM-2015-0008
Judith Masthoff, Group Recommender Systems: Combining Individual Models Recommender Systems Handbook. pp. 677- 702 ,(2011) , 10.1007/978-0-387-85820-3_21
Mark O’connor, Dan Cosley, Joseph A Konstan, John Riedl, None, PolyLens: A Recommender System for Groups of Users ECSCW 2001. pp. 199- 218 ,(2001) , 10.1007/0-306-48019-0_11
Cecilia Mascolo, Anastasios Noulas, Massimiliano Pontil, Salvatore Scellato, Exploiting semantic annotations for clustering geographic areas and users in location-based social networks international conference on weblogs and social media. ,(2011)
Irwin King, Haiqin Yang, Michael R. Lyu, Chen Cheng, Where you like to go next: successive point-of-interest recommendation international joint conference on artificial intelligence. pp. 2605- 2611 ,(2013)
Andrej Karpathy, Li Fei-Fei, Deep visual-semantic alignments for generating image descriptions computer vision and pattern recognition. pp. 3128- 3137 ,(2015) , 10.1109/CVPR.2015.7298932
Çaglar Gülçehre, Yoshua Bengio, Yoshua Bengio, Yoshua Bengio, KyungHyun Cho, Junyoung Chung, Empirical evaluation of gated recurrent neural networks on sequence modeling arXiv: Neural and Evolutionary Computing. ,(2014)
Li Chen, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, Francesco Ricci, Giovanni Semeraro, Human Decision Making and Recommender Systems Ksii Transactions on Internet and Information Systems. ,vol. 3, pp. 611- 648 ,(2013) , 10.1145/2533670.2533675
Jürgen Schmidhuber, Deep learning in neural networks Neural Networks. ,vol. 61, pp. 85- 117 ,(2015) , 10.1016/J.NEUNET.2014.09.003