Managing Natural Noise in Recommender Systems
Tipo de publicación: International Conference
Año de publicación: 2016
Autores: Luis Martínez
Director: Jorge Castro, Raciel Yera
Tipo: 5th International Conference on Theory and Practice of Natural Computing (TPNC 2016)
Editorial: Springer
Paginación: 3-17
Numero ISSN: 0302-9743
ISBN Number: 978-3-319-49000-7
Lugar de publicación: Sendai (Japan)
Resumen: E-commerce customers demand quick and easy access to suitable products in large purchase spaces. To support and facilitate this purchasing process to users, recommender systems (RSs) help them to find out the information that best fits their preferences and needs in an overloaded search space. These systems require the elicitation of customers preferences. However, this elicitation process is not always precise either correct because of external factors such as human errors, uncertainty, human beings inherent inconsistency and so on. Such a problem in RSs is known as\&nbsp;<em>natural noise</em>\&nbsp;(NN) and can negatively bias recommendations, which leads to poor users experience. Different proposals have been presented to deal with natural noise in RSs. Several of them require additional interaction with customers. Others just remove noisy information. Recently, new NN approaches dealing with the ratings stored in the user/item rating matrix have raised to deal with NN in a better and simpler way. This contribution is devoted to provide a brief review of the latter approaches revising crisp and fuzzy approaches for dealing with NN in RSs. Eventually it points out as a future research the management of NN in other recommendation scenarios as group RSs.
DOI: 10.1007/978-3-319-49001-4