Transforming Geo-Referenced Data in Contextual Information for Context-Aware Recommender Systems

A recommender system can be defined as an information filtering technology which can be used to output a ranking of items (e.g. products, places, etc) that are likely to be of interest to a user. Context-aware recommender systems makes recommendations by incorporating contextual information into the recommendation process. However, there is a lack of automatic methods to obtain contextual information for such systems. In this work, we have proposed to apply clustering techniques to transform geo-referenced data (i.e. latitude and longitude) in contextual information (i.e. regions) to feed the contextual systems. We have evaluated our proposal in the Yelp dataset, which showed evidences that our contextual information can provide better recommendations.