Aggregated Temporal Tensor Factorization Model for Point-of-interest Recommendation

Temporal influence plays an important role in a point-of-interest (POI) recommendation system that suggests POIs for users in location-based social networks (LBSNs). Previous studies observe that the user mobility in LBSNs exhibits distinct temporal features, summarized as periodicity, consecutiveness, and non-uniformness. By capturing the observed temporal features, a variety of systems are proposed to enhance POI recommendation. However, previous work does not model the three features together. More importantly, we observe that the temporal influence exists at different time scales, yet this observation cannot be modeled in prior work. In this paper, we propose an Aggregated Temporal Tensor Factorization (ATTF) model for POI recommendation to capture the three temporal features together, as well as at different time scales. Specifically, we employ temporal tensor factorization to model the check-in activity, subsuming the three temporal features together. Furthermore, we exploit a linear combination operator to aggregate temporal latent features’ contributions at different time scales. Experiments on two real life datasets show that the ATTF model achieves better performance than models capturing temporal influence at single scale. In addition, our proposed ATTF model outperforms the state-of-the-art methods.

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

[2]  Huan Liu,et al.  gSCorr: modeling geo-social correlations for new check-ins on location-based social networks , 2012, CIKM.

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

[4]  Chi-Yin Chow,et al.  GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations , 2015, SIGIR.

[5]  Chi-Yin Chow,et al.  iGSLR: personalized geo-social location recommendation: a kernel density estimation approach , 2013, SIGSPATIAL/GIS.

[6]  Huan Liu,et al.  Exploring temporal effects for location recommendation on location-based social networks , 2013, RecSys.

[7]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

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

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

[10]  Chao Liu,et al.  Recommender systems with social regularization , 2011, WSDM '11.

[11]  Yizhou Sun,et al.  LCARS: a location-content-aware recommender system , 2013, KDD.

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

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

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

[15]  Michael R. Lyu,et al.  Capturing Geographical Influence in POI Recommendations , 2013, ICONIP.

[16]  Lars Schmidt-Thieme,et al.  Pairwise interaction tensor factorization for personalized tag recommendation , 2010, WSDM '10.

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

[18]  Nicholas Jing Yuan,et al.  Content-Aware Collaborative Filtering for Location Recommendation Based on Human Mobility Data , 2015, 2015 IEEE International Conference on Data Mining.