DI-UMONS : Dépôt institutionnel de l’université de Mons

Recherche transversale
(titres de publication, de périodique et noms de colloque inclus)
2018-09-14 - Colloque/Article dans les actes avec comité de lecture - Anglais - page(s)

Aliannejadi Mohammad, Rafailidis Dimitrios , Crestani Fabio, "A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion" in International Conference on the Theory of Information Retrieval, Tianjin, China, 2018

  • Codes CREF : Informatique générale (DI1162)
  • Unités de recherche UMONS : Systèmes d'information (S832)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech), Institut de Recherche sur les Systèmes Complexes (Complexys)
  • Centres UMONS : Modélisation mathématique et informatique (CREMMI)

Abstract(s) :

(Anglais) Recommending venues plays a critical rule in satisfying users’ needs on location-based social networks. Recent studies have explored theidea of adopting collaborative ranking (CR) for recommendation, combining the idea of learning to rank and collaborative €filtering. However, CR su‚ffers from the sparsity problem, mainly because it associates similar users based on exact matching of the venues in their check-in history. Even though research in collaborative €filtering has shown that considering auxiliary information such as geographical influence, helps the model to alleviate the sparsity problem, the same direction still needs to be explored in CR. In this work, we present a CR framework that focuses on the top of the ranked list while integrating an arbitrary number of similarity functions between venues as it learns the model’s parameters. We further introduce three example similarity measures based on venues’ contents and locations. Incorporating cross-venue similarity measures into the model enhances the latent associations between users as similar venues are also taken into account while associating users with each other. Our experiments on the TREC Contextual Suggestion dataset show that our proposed CR model beats other state-of-the-art venue suggestion methods.