Analysing Collaborative Filtering algorithms in a multi-agent environment

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2014

Autores

Cunha, T (Autor) Outra; Rosaldo J. F. Rossetti (Autor) FEUP; Carlos Soares (Autor) FEUP

Ata de Conferência Internacional

Título: ESM'2014 - The 2014 European Simulation and Modelling Conference, Modelling and Simulation '2014

Páginas: 135-139

ESM'2014 - 28th European Simulation and Modelling Conference, Porto, Portugal, October 22-24, 2014

Indexação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00A-5D2

Abstract (EN): The huge amount of online information deprives the user to keep up with his/hers interests and preferences, Recommender Systems appeared to solve this problem, by employing social behavioural paradigms in order to recommend potentially interesting items to users, Among the several kinds of Recommender Systems, one of the most mature and most used in real world applications are known as Collaborative Filtering. These methods recommend items based on the preferences of similar-users, using only a user-item rating matrix. In this pa¿ per we explain a methodology to use Multi¿Agent based simulation to study the evolution of the data rating matrix and its effect on the performance of several Collaborative Filtering algorithms. Our results show that the best performing methods are user-based and item-based Collaborative Filtering and that the average algorithm performance is surprisingly constant for different rating schemes.

Idioma: Inglês

Tipo (Avaliação Docente): Científica