Towards real-time demand-aware sequential POI recommendation

Abstract Next point-of-interest (POI) recommendation has gained growing attention in recent years due to the emergence of location-based social networks (LBSN) services. Most existing approaches focus on learning user’s preferences to POIs from check-in records and recommend a POI to visit next given his/her previously visited POIs. However, the user’s visiting behavior is not only driven by user preferences in real-world scenarios. The real-time demand is another crucial factor to determine the user’s visiting behaviors, which is usually neglected in established approaches. In this paper, we propose a new next point-of-interest (POI) recommendation method, called DSPR, by exploring user’s preferences and real-time demand simultaneously. To model the real-time demand, different kinds of contextual information are exploited, such as absolute time, POI–POI transition time/distance, and the types of POIs. By incorporating user’s preferences, these contextual factors are further modeled and learned automatically with an attention-based recurrent neural network model to support the final next POI recommendation. Experiments on three real-world check-in datasets show that DSPR has better recommendation performance compared with many state-of-the-art methods.

[1]  Lina Yao,et al.  Context-aware Point-of-Interest Recommendation Using Tensor Factorization with Social Regularization , 2015, SIGIR.

[2]  Chunyan Miao,et al.  Exploiting Geographical Neighborhood Characteristics for Location Recommendation , 2014, CIKM.

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

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

[5]  Victor C. S. Lee,et al.  iMCRec: A multi-criteria framework for personalized point-of-interest recommendations , 2019, Inf. Sci..

[6]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[7]  Huan Liu,et al.  What Your Images Reveal: Exploiting Visual Contents for Point-of-Interest Recommendation , 2017, WWW.

[8]  Julian J. McAuley,et al.  Translation-based Recommendation , 2017, RecSys.

[9]  Thomas Hofmann,et al.  Unifying collaborative and content-based filtering , 2004, ICML.

[10]  Byeong Man Kim,et al.  A new approach for combining content-based and collaborative filters , 2003, Journal of Intelligent Information Systems.

[11]  Ke Wang,et al.  Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding , 2018, WSDM.

[12]  Yu Zheng,et al.  ORec: An Opinion-Based Point-of-Interest Recommendation Framework , 2015, CIKM.

[13]  Maoguo Gong,et al.  Multi-objective optimization for location-based and preferences-aware recommendation , 2020, Inf. Sci..

[14]  Mao Ye,et al.  Location recommendation for location-based social networks , 2010, GIS '10.

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

[16]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[17]  Ruifeng Ding,et al.  RecNet: a deep neural network for personalized POI recommendation in location-based social networks , 2018, Int. J. Geogr. Inf. Sci..

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

[19]  Gao Cong,et al.  Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences , 2014, CIKM.

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

[21]  Julian J. McAuley,et al.  Self-Attentive Sequential Recommendation , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[22]  Zhihai Rong,et al.  Correlation between social proximity and mobility similarity , 2016, Scientific Reports.

[23]  Chang-Tien Lu,et al.  Learning evolving user’s behaviors on location-based social networks , 2020, GeoInformatica.

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

[25]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[26]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[27]  Tao Mei,et al.  Author Topic Model-Based Collaborative Filtering for Personalized POI Recommendations , 2015, IEEE Transactions on Multimedia.

[28]  Tat-Seng Chua,et al.  Neural Collaborative Filtering , 2017, WWW.

[29]  Hui Xiong,et al.  Unified Point-of-Interest Recommendation with Temporal Interval Assessment , 2016, KDD.

[30]  Felix Klanner,et al.  Modeling hierarchical category transition for next POI recommendation with uncertain check-ins , 2020, Inf. Sci..

[31]  Chao Zhang,et al.  SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories , 2017, CIKM.

[32]  Xueming Qian,et al.  Long- and Short-term Preference Learning for Next POI Recommendation , 2019, CIKM.

[33]  Julian J. McAuley,et al.  Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

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

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

[36]  Lejian Liao,et al.  Category-aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking , 2017, IJCAI.

[37]  Michael R. Lyu,et al.  Geo-Teaser: Geo-Temporal Sequential Embedding Rank for Point-of-interest Recommendation , 2016, WWW.

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

[39]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.

[40]  Maoguo Gong,et al.  A two-step personalized location recommendation based on multi-objective immune algorithm , 2019, Inf. Sci..

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