Personalized recommendation of Points of Interest (POIs) plays a key role in satisfying users on Location-Based Social Networks (LBSNs). In this article, we propose a probabilistic model to find the mapping between user-annotated tags and locations' taste keywords. Furthermore, we introduce a dataset on locations' contextual appropriateness and demonstrate its usefulness in predicting the contextual relevance of locations. We investigate four approaches to use our proposed mapping for addressing the data sparsity problem: one model to reduce the dimensionality of location taste keywords and three models to predict user tags for a new location. Moreover, we present different scores calculated from multiple LBSNs and show how we incorporate new information from the mapping into a POI recommendation approach. Then, the computed scores are integrated using learning to rank techniques. The experiments on two TREC datasets show the effectiveness of our approach, beating state-of-the-art methods. Mohammad Aliannejadi and Fabio Crestani. 2018. Personalized Context-Aware Point of Interest Recommendation.
In addition, Aliannejadi and Crestani REF utilized a probabilistic model to construct the mapping between user tags and location taste keywords, which made it possible to exploit various directions to address the data sparsity problem for POI recommendation.
Personalized Context-Aware Point of Interest Recommendation
TOIS
Computer Science