A fuzzy approach for natural noise management in group recommender systems

Information filtering is a key task in scenarios with information overload. Group Recommender Systems (GRSs) filter content regarding groups of users preferences and needs. Both the recommendation method and the available data influence recommendation quality. Most researchers improved group recommendations through the proposal of new algorithms. However, it has been pointed out that the ratings are not always right because users can introduce noise due to factors such as context of rating or user's errors. This introduction of errors without malicious intentions is named natural noise, and it biases the recommendation. Researchers explored natural noise management in individual recommendation, but few explored it in GRSs. The latter ones apply crisp techniques, which results in a rigid management. In this work, we propose Natural Noise Management for Groups based on Fuzzy Tools (NNMG-FT). NNMG-FT flexibilises the detection and correction of the natural noise to perform a better removal of natural noise influence in the recommendation, hence, the recommendations of a latter GRS are then improved.