A fuzzy model for managing natural noise in recommender systems
Type of publication: International Journal
Year of publication: 2016
Authors: Raciel Yera
Director: Jorge Castro, Luis Martínez
Type: Applied Soft Computing
Publication date: 03/2016
Volumen: 40
Pagination: 187-198
ISSN number: 1568-4946
Abstract: Abstract E-commerce customers demand quick and easy access to products in large search spaces according to their needs and preferences. To support and facilitate this process, recommender systems (RS) based on user preferences have recently played a key role. However the elicitation of customers preferences is not always precise either correct, because of external factors such as human errors, uncertainty and vagueness proper of human beings and so on. Such a problem in \RS\ is known as natural noise and can bias customers recommendations. Despite different proposals have been presented to deal with natural noise in \RS\ none of them is able to manage properly the inherent uncertainty and vagueness of customers preferences. Hence, this paper is devoted to a new fuzzy method for managing in a flexible and adaptable way such uncertainty of natural noise in order to improve recommendation accuracy. Eventually a case study is performed to show the improvements produced by this fuzzy method regarding previous proposals.
URL: http://www.sciencedirect.com/science/article/pii/S1568494615007048
DOI: http://dx.doi.org/10.1016/j.asoc.2015.10.060
Quartile:
Q1
Índice de impacto:
2.810