Learning Geo-Social User Topical Profiles with Bayesian Hierarchical User Factorization Conference Paper uri icon


  • © 2018 ACM. Understanding user interests and expertise is a vital component toward creating rich user models for information personalization in social media, recommender systems and web search. To capture the pair-wise interactions between geo-location and user's topical profile in social-spatial systems, we propose the modeling of fine-grained and multi-dimensional user geo-topic profiles. We then propose a two-layered Bayesian hierarchical user factorization generative framework to overcome user heterogeneity and another enhanced model integrated with user's contextual information to alleviate multi-dimensional sparsity. Through extensive experiments, we find the proposed model leads to a 5 extasciitilde13% improvement in precision and recall over the alternative baselines and an addi ional 6 extasciitilde11% improvement with the integration of user's contexts.

author list (cited authors)

  • Lu, H., Niu, W., & Caverlee, J.

citation count

  • 3

editor list (cited editors)

  • Collins-Thompson, K., Mei, Q., Davison, B. D., Liu, Y., & Yilmaz, E.

publication date

  • June 2018


  • ACM  Publisher