Personalized Context-Aware Point of Interest Recommendation
This addresses data sparsity in POI recommendation for users on location-based social networks, but it appears incremental as it builds on existing methods with new mappings and datasets.
The paper tackles the problem of personalized POI recommendation on LBSNs by proposing a probabilistic model to map user tags to location keywords and introducing a dataset for contextual appropriateness, with experiments on TREC datasets showing it beats state-of-the-art methods.
Personalized recommendation of Points of Interest (POIs) plays a key role in satisfying users on Location-Based Social Networks (LBSNs). In this paper, 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.