Personalized Ranking for Context-Aware Venue Suggestion

Making personalized and context-aware suggestions of venues to the users is very crucial in venue recommendation. These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations. In this paper we present a novel user-modeling approach which relies on a set of scoring functions for making personalized suggestions of venues based on venues content and reviews as well as users context. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, prove that our methodology outperforms state-of-the-art approaches by a significant margin.Comment: The 32nd ACM SIGAPP Symposium On Applied Computing (SAC), Marrakech, Morocco, April 4-6, 201

A Personalised Recommendation System for Context-Aware Suggestions

The recently introduced TREC Contextual Suggestion track proposes the problem of suggesting contextually relevant places to a user visiting a new city based on his/her preferences and the location of the new city. In this paper we introduce a more sophisticated approach to this problem which very carefully constructs user profiles in order to provide more accurate and relevant recommendations. Based on the track evaluations we demonstrate that our system not only significantly outperforms a baseline method but also performs very well in comparison to other runs submitted to the track, managing to achieve the best results in nearly half of all test contexts