Discovering Subsequence Patterns for Next POI Recommendation

Next Point-of-Interest (POI) recommendation plays an important role in location-based services. State-of-the-art methods learn the POI-level sequential patterns in the user’s check-in sequence but ignore the subsequence patterns that often represent the socio-economic activities or coherence of preference of the users. However, it is challenging to integrate the semantic subsequences due to the difficulty to predefine the granularity of the complex but meaningful subsequences. In this paper, we propose Adaptive Sequence Partitioner with Power-law Attention (ASPPA) to automatically identify each semantic subsequence of POIs and discover their sequential patterns. Our model adopts a state-based stacked recurrent neural network to hierarchically learn the latent structures of the user’s check-in sequence. We also design a power-law attention mechanism to integrate the domain knowledge in spatial and temporal contexts. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.

[1]  Ling Chen,et al.  Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation , 2017, IEEE Transactions on Knowledge and Data Engineering.

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

[3]  Weiqing Wang,et al.  TPM: A Temporal Personalized Model for Spatial Item Recommendation , 2018, ACM Trans. Intell. Syst. Technol..

[4]  Thomas L. Griffiths,et al.  Hierarchical Topic Models and the Nested Chinese Restaurant Process , 2003, NIPS.

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

[6]  Yoshua Bengio,et al.  Hierarchical Recurrent Neural Networks for Long-Term Dependencies , 1995, NIPS.

[7]  Ling Chen,et al.  SPORE: A sequential personalized spatial item recommender system , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[8]  Zi Huang,et al.  Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices , 2020, WWW.

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

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

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

[12]  Jiawei Han,et al.  Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation , 2017, KDD.

[13]  Zi Huang,et al.  Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation , 2016, ACM Trans. Inf. Syst..

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

[15]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[16]  Wei Zhang,et al.  Location and Time Aware Social Collaborative Retrieval for New Successive Point-of-Interest Recommendation , 2015, CIKM.

[17]  Gao Cong,et al.  An Experimental Evaluation of Point-of-interest Recommendation in Location-based Social Networks , 2017, Proc. VLDB Endow..

[18]  Yoshua Bengio,et al.  Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations , 2016, ICLR.

[19]  Yoshua Bengio,et al.  Hierarchical Multiscale Recurrent Neural Networks , 2016, ICLR.

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

[21]  Deng Cai,et al.  What to Do Next: Modeling User Behaviors by Time-LSTM , 2017, IJCAI.

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

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

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

[25]  Pengpeng Zhao,et al.  LC-RNN: A Deep Learning Model for Traffic Speed Prediction , 2018, IJCAI.