Most existing context aware recommender systems primarily use a combination of ratings data, content data like features or attributes of the product or service, context data like location or time and social network data. In this paper, we propose a novel approach for refining the recommendations made by location-aware recommender systems based on user motivations for checking in at locations in location based social networks. Based on a classification that classifies user’s motivation for checking in at a Point Of Interest into seven categories we propose an approach that will help refine recommendations in a way which can be better explained to the user. We also show the applicability of our approach by analyzing a dataset extracted from Foursquare.
|Journal||CEUR Workshop Proceedings|