A fuzzy approach for natural noise management in group recommender systems
Type of publication: International Journal
Year of publication: 2018
Authors: Jorge Castro
Director: Raciel Yera, Luis Martínez
Type: Expert Systems with Applications
Publication date: 03/ 2018
Volumen: 94
Pagination: 237-249
ISSN number: 0957-4174
Abstract: Abstract 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 users 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.
URL: https://www.sciencedirect.com/science/article/pii/S095741741730742X
DOI: https://doi.org/10.1016/j.eswa.2017.10.060
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