STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation

Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation. Recent Recurrent Neural Network (RNN) based approaches learn POI-POI relationships in a local view based on independent user visit sequences. This limits the model's ability to directly connect and learn across users in a global view to recommend semantically trained POIs. In this work, we propose a Spatial-Temporal-Preference User Dimensional Graph Attention Network (STP-UDGAT), a novel explore-exploit model that concurrently exploits personalized user preferences and explores new POIs in global spatial-temporal-preference (STP) neighbourhoods, while allowing users to selectively learn from other users. In addition, we propose random walks as a masked self-attention option to leverage the STP graphs' structures and find new higher-order POI neighbours during exploration. Experimental results on six real-world datasets show that our model significantly outperforms baseline and state-of-the-art methods.

[1]  Gary LaFree,et al.  Introducing the Global Terrorism Database , 2007 .

[2]  Daqing Zhang,et al.  NationTelescope: Monitoring and visualizing large-scale collective behavior in LBSNs , 2015, J. Netw. Comput. Appl..

[3]  Donghyeon Park,et al.  Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation , 2018, IJCAI.

[4]  Charu C. Aggarwal,et al.  Ensemble-Spotting: Ranking Urban Vibrancy via POI Embedding with Multi-view Spatial Graphs , 2018, SDM.

[5]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[6]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[7]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[8]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[9]  Tieniu Tan,et al.  Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts , 2016, AAAI.

[10]  Lars Schmidt-Thieme,et al.  Factorizing personalized Markov chains for next-basket recommendation , 2010, WWW '10.

[11]  Yifeng Zeng,et al.  Personalized Ranking Metric Embedding for Next New POI Recommendation , 2015, IJCAI.

[12]  Weitong Chen,et al.  Learning Graph-based POI Embedding for Location-based Recommendation , 2016, CIKM.

[13]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[14]  Wei Guo,et al.  A Category-Aware Deep Model for Successive POI Recommendation on Sparse Check-in Data , 2020, WWW.

[15]  Fei Wu,et al.  HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction , 2018, IJCAI.

[16]  Lang Jiao,et al.  Golang-Based POI Discovery and Recommendation in Real Time , 2019, 2019 20th IEEE International Conference on Mobile Data Management (MDM).

[17]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.

[18]  Michael R. Lyu,et al.  Where You Like to Go Next: Successive Point-of-Interest Recommendation , 2013, IJCAI.

[19]  Kun Gai,et al.  Learning Tree-based Deep Model for Recommender Systems , 2018, KDD.

[20]  Yanjie Fu,et al.  Unifying Inter-region Autocorrelation and Intra-region Structures for Spatial Embedding via Collective Adversarial Learning , 2019, KDD.

[21]  Lejian Liao,et al.  Inferring a Personalized Next Point-of-Interest Recommendation Model with Latent Behavior Patterns , 2016, AAAI.

[22]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[23]  Xiaohui Yu,et al.  MPE: a mobility pattern embedding model for predicting next locations , 2018, World Wide Web.

[24]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[25]  Xing Xie,et al.  Session-based Recommendation with Graph Neural Networks , 2018, AAAI.

[26]  Yixin Cao,et al.  KGAT: Knowledge Graph Attention Network for Recommendation , 2019, KDD.

[27]  Fuzhen Zhuang,et al.  Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation , 2019, AAAI.

[28]  Nadia Magnenat-Thalmann,et al.  Time-aware point-of-interest recommendation , 2013, SIGIR.

[29]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[30]  Hui Xiong,et al.  Adversarial Substructured Representation Learning for Mobile User Profiling , 2019, KDD.

[31]  Quoc Viet Hung Nguyen,et al.  Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation , 2020, AAAI.