POI2Vec: Geographical Latent Representation for Predicting Future Visitors

With the increasing popularity of location-aware social media applications, Point-of-Interest (POI) recommendation has recently been extensively studied. However, most of the existing studies explore from the users' perspective, namely recommending POIs for users. In contrast, we consider a new research problem of predicting users who will visit a given POI in a given future period. The challenge of the problem lies in the difficulty to effectively learn POI sequential transition and user preference, and integrate them for prediction. In this work, we propose a new latent representation model POI2Vec that is able to incorporate the geographical influence, which has been shown to be very important in modeling user mobility behavior. Note that existing representation models fail to incorporate the geographical influence. We further propose a method to jointly model the user preference and POI sequential transition influence for predicting potential visitors for a given POI. We conduct experiments on 2 real-world datasets to demonstrate the superiority of our proposed approach over the state-of-the-art algorithms for both next POI prediction and future user prediction.

[1]  Xin Rong,et al.  word2vec Parameter Learning Explained , 2014, ArXiv.

[2]  Pengfei Wang,et al.  Learning Hierarchical Representation Model for NextBasket Recommendation , 2015, SIGIR.

[3]  Gao Cong,et al.  SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews , 2015, 2015 IEEE 31st International Conference on Data Engineering.

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

[5]  Quoc V. Le,et al.  Distributed Representations of Sentences and Documents , 2014, ICML.

[6]  Xing Xie,et al.  GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation , 2014, KDD.

[7]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

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

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

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

[11]  Michael R. Lyu,et al.  Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks , 2012, AAAI.

[12]  Yoshua Bengio,et al.  Hierarchical Probabilistic Neural Network Language Model , 2005, AISTATS.

[13]  Richang Hong,et al.  Point-of-Interest Recommendations: Learning Potential Check-ins from Friends , 2016, KDD.

[14]  Xiaoli Li,et al.  Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation , 2015, SIGIR.

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

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

[17]  Nadia Magnenat-Thalmann,et al.  Who, where, when and what: discover spatio-temporal topics for twitter users , 2013, KDD.

[18]  Chunyan Miao,et al.  Personalized point-of-interest recommendation by mining users' preference transition , 2013, CIKM.

[19]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[20]  Xin Liu,et al.  Exploring the Context of Locations for Personalized Location Recommendations , 2016, IJCAI.

[21]  Geoffrey E. Hinton,et al.  A Scalable Hierarchical Distributed Language Model , 2008, NIPS.

[22]  Michael R. Lyu,et al.  STELLAR: Spatial-Temporal Latent Ranking for Successive Point-of-Interest Recommendation , 2016, AAAI.

[23]  Zhe Zhu,et al.  What's Your Next Move: User Activity Prediction in Location-based Social Networks , 2013, SDM.

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

[25]  Mao Ye,et al.  Exploiting geographical influence for collaborative point-of-interest recommendation , 2011, SIGIR.

[26]  Chi-Yin Chow,et al.  LORE: exploiting sequential influence for location recommendations , 2014, SIGSPATIAL/GIS.

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