Authors:
Olatz Arbelaitz
;
Ibai Gurrutxaga
;
Aizea Lojo
;
Javier Muguerza
;
Jesús M. Pérez
and
Iñigo Perona
Affiliation:
University of the Basque Country UPV-EHU, Spain
Keyword(s):
Adaptive Web, Link Prediction, User Profile, Collaborative Filtering, Machine Learning, Web Usage Mining, Web Content Mining, Semantics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Collaborative Filtering
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Text and Semi-Structured Data
;
Soft Computing
;
Symbolic Systems
;
User Profiling and Recommender Systems
;
Web Mining
Abstract:
Websites are important tools for tourism destinations. The adaptation of the websites to the users’ preferences and requirements will turn the websites into more effective tools. Using machine learning techniques to build user profiles allows us to take into account their real preferences. This paper presents the first approach of a system that, based on a collaborative filtering approach, adapts a tourism website to improve the browsing experience of the users: it generates automatically interesting links for new users. In this work we first build a system based just on the usage information stored in web log files (common log format) and then combine it with the web content information to improve the performance of the system. The use of content information not only improves the results but it also offers very useful information about the users’ interests to travel agents.