Personalized recommendations of locally interesting venues to tourists via cross-region community matching

摘要

A challenging problem is presented in this paper: recommending interesting places to tourists. The authors address the different challenges to provide recommendations that can be characterized by novelty and high relevance. To achieve that, they propose a Bayesian approach to extract the core social dimensions of tourists at different geographical locations after dividing people into various interest groups with high degrees of similarity. Different latent factorization techniques are designed to provide the best possible recommendations. The main techniques presented include non-negative matrix factorization and Bayesian probabilistic matrix factorization.