User-contributed relevance and nearest neighbor queries
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Novel Web technologies and resulting applications have lead to a participatory data ecosystem that when utilized properly will lead to more rewarding services. In this work, we investigate the case of Location-based Services and specifically of how to improve the typical location-based Point-Of-Interest (POI) request processed as a k-Nearest-Neighbor query. This work introduces Links-of-interest (LOI) between POIs as a means to increase the relevance and overall result quality of such queries. By analyzing user-contributed content in the form of travel blogs, we establish the overall popularity of a LOI, i.e., how frequently the respective POI pair is mentioned in the same context. Our contribution is a query processing method for so-called k-Relevant Nearest Neighbor (k-RNN) queries that considers spatial proximity in combination with LOI information to retrieve close-by and relevant (as judged by the crowd) POIs. Our method is based on intelligently combining indices for spatial data (a spatial grid) and for relevance data (a graph) during query processing. An experimental evaluation using real and synthetic data establishes that our approach efficiently solves the k-RNN problem when compared to existing methods.