Extracting Geospatial Preferences Using Relational Neighbors

  • Leandro Balby Marinho Federal University of Campina Grande
  • Thomas Sandholm Federal University of Campina Grande
  • Cláudio de Souza Baptista Federal University of Campina Grande
  • Iury Nunes Federal University of Campina Grande
  • Caio Nóbrega Federal University of Campina Grande
  • Jordão Araújo Federal University of Campina Grande
Keywords: Recommender Systems, Location-Based Services, Collaborative Filtering, Geographic-Aware

Abstract

 

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 on data collected from the Panoramio photo sharing site and provide empirical evidence that many of the proposed algorithms outperform existing location-aware recommender algorithms.

 

Published
2012-09-27
Section
SBBD Articles