An experimental comparative study of web mining methods for recommender systems

  1. Saddys Segrera 1
  2. María N. Moreno 1
  1. 1 Department of Computing and Automatic, University of Salamanca, Salamanca, Spain
Actas:
DIWED'06: Proceedings of the 6th WSEAS International Conference on Distance Learning and Web Engineering

Editorial: World Scientific and Engineering Academy and Society (WSEAS)

ISBN: 978-960-8457-53-9

Año de publicación: 2006

Páginas: 56-61

Congreso: DIWED'06: Proceedings of the 6th WSEAS International Conference on Distance Learning and Web Engineering, Lisbon Portugal September 22 - 24, 2006

Tipo: Aportación congreso

GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

An essential goal of the present web engineering is the development of efficient and competitive applications. This objective can be achieved by building recommender systems endowed with suitable web mining algorithms. Multiclassifiers are reliable data mining models that have been hardly used in the web system area. The paper presents a comparative study among different simple classifiers and multiclassifiers using a dataset from MovieLens recommender system. The aim of the work is to identify when the use of multiclassifiers in this type of systems is efficient.