Abstract
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.
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Index Terms
- Personalized Context-Aware Point of Interest Recommendation
Recommendations
Time-aware point-of-interest recommendation
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrievalThe availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to ...
Exploiting geographical influence for collaborative point-of-interest recommendation
SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information RetrievalIn this paper, we aim to provide a point-of-interests (POI) recommendation service for the rapid growing location-based social networks (LBSNs), e.g., Foursquare, Whrrl, etc. Our idea is to explore user preference, social influence and geographical ...
Evaluation of Social, Geography, Location Effects for Point-of-Interest Recommendation
ICDMW '13: Proceedings of the 2013 IEEE 13th International Conference on Data Mining WorkshopsRecently, location-based social network services have become very popular. Therefore, point of interests (POIs) recommendation has also become a promising and hot research problem. In POIs recommendation, the number of locations could be more than the ...
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