Reducing offline evaluation bias of collaborative filtering algorithms

Arnaud de Myttenaere (1, 2) , Boris Golden (2) , Bénédicte Le Grand (3) , Fabrice Rossi (1)

1 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)
2 Viadeo - Viadeo SA
3 CRI - Centre de Recherche en Informatique de Paris 1

Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical data (via offline evaluation). This paper presents a new application of a weighted offline evaluation to reduce this bias for collaborative filtering algorithms.

Mots clés: Recommendation System; Off-line Evaluation

Domaines: Machine Learning [stat.ML]

Arnaud de Myttenaere, Boris Golden, Bénédicte Le Grand, Fabrice Rossi. Reducing offline evaluation bias of collaborative filtering algorithms. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. pp.137-142. ⟨hal-01163390⟩