Learning User's Intrinsic and Extrinsic Interests for Point-of-Interest Recommendation: A Unified Approach

Point-of-Interest (POI) recommendation has been an important service on location-based social networks. However, it is very challenging to generate accurate recommendations due to the complex nature of user's interest in POI and the data sparseness. In this paper, we propose a novel unified approach that could effectively learn fine-grained and interpretable user's interest, and adaptively model the missing data. Specifically, a user's general interest in POI is modeled as a mixture of her intrinsic and extrinsic interests, upon which we formulate the ranking constraints in our unified recommendation approach. Furthermore, a self-adaptive location-oriented method is proposed to capture the inherent property of missing data, which is formulated as squared error based loss in our unified optimization objective. Extensive experiments on real-world datasets demonstrate the effectiveness and advantage of our approach.

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

[2]  Masoud Ardakani,et al.  Separating-Plane Factorization Models: Scalable Recommendation from One-Class Implicit Feedback , 2016, CIKM.

[3]  Tat-Seng Chua,et al.  Fast Matrix Factorization for Online Recommendation with Implicit Feedback , 2016, SIGIR.

[4]  Yuh-Jye Lee,et al.  SSVM: A Smooth Support Vector Machine for Classification , 2001, Comput. Optim. Appl..

[5]  E. Deci,et al.  Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions. , 2000, Contemporary educational psychology.

[6]  George Karypis,et al.  FISM: factored item similarity models for top-N recommender systems , 2013, KDD.

[7]  Steffen Rendle,et al.  Improving pairwise learning for item recommendation from implicit feedback , 2014, WSDM.

[8]  Michael J. Todd,et al.  Mathematical programming , 2004, Handbook of Discrete and Computational Geometry, 2nd Ed..

[9]  Suhrid Balakrishnan,et al.  Collaborative ranking , 2012, WSDM '12.

[10]  Richang Hong,et al.  A Spatial-Temporal Probabilistic Matrix Factorization Model for Point-of-Interest Recommendation , 2016, SDM.

[11]  Markus Zanker,et al.  Proceedings of the fourth ACM conference on Recommender systems , 2010, RecSys 2010.

[12]  Giacomo Mauro DAriano.  The Journal of Personality and Social Psychology. , 2002 .

[13]  Olvi L. Mangasarian,et al.  Smoothing methods for convex inequalities and linear complementarity problems , 1995, Math. Program..

[14]  Qiang Yang,et al.  One-Class Collaborative Filtering , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[15]  Meng Wang,et al.  A Relaxed Ranking-Based Factor Model for Recommender System from Implicit Feedback , 2016, IJCAI.

[16]  Li Chen,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence GBPR: Group Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering , 2022 .

[17]  Feng Xu,et al.  Dual-Regularized One-Class Collaborative Filtering , 2014, CIKM.

[18]  Huayu Li,et al.  Point-of-Interest Recommender Systems: A Separate-Space Perspective , 2015, 2015 IEEE International Conference on Data Mining.

[19]  Padhraic Smyth,et al.  Modeling human location data with mixtures of kernel densities , 2014, KDD.

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

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

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

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

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

[25]  Chengqi Zhang,et al.  Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2015, KDD.

[26]  Domonkos Tikk,et al.  Fast als-based matrix factorization for explicit and implicit feedback datasets , 2010, RecSys '10.