Extracting Geospatial Preferences Using Relational Neighbors
This addresses the problem of improving recommendation accuracy in location-based social media for users, though it appears incremental as it builds on existing location-aware methods.
The paper tackles the challenge of extracting user preferences from large-scale collaborative geo-referenced data by proposing a geospatial approach using a relational graph to capture geographic context, and it shows that many of the proposed algorithms outperform existing location-aware recommender algorithms in experiments on two real-world datasets.
With the increasing popularity of location-based social media applications and devices that automatically tag generated content with locations, large repositories of collaborative geo-referenced data are appearing on-line. Efficiently extracting user preferences from these data to determine what information to recommend is challenging because of the sheer volume of data as well as the frequency of updates. Traditional recommender systems focus on the interplay between users and items, but ignore contextual parameters such as location. In this paper we take a geospatial approach to determine locational preferences and similarities between users. We propose to capture the geographic context of user preferences for items using a relational graph, through which we are able to derive many new and state-of-the-art recommendation algorithms, including combinations of them, requiring changes only in the definition of the edge weights. Furthermore, we discuss several solutions for cold-start scenarios. Finally, we conduct experiments using two real-world datasets and provide empirical evidence that many of the proposed algorithms outperform existing location-aware recommender algorithms.