By Topic

Recommendation Rule Extraction by a Neuro-Fuzzy Approach

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Castellano, G. ; Dept. of Inf., Univ. of Bari "A. Moro", Bari ; Fanelli, A.M. ; Torsello, M.A.

Recommender systems attempt to predict the needs of Web users and provide them with recommendations to personalize their online experience. In this paper, we propose a neuro-fuzzy approach for the extraction of a recommendation model from usage data encoding user navigational behaviors. Such model is expressed as a set of fuzzy rules which may be exploited to provide personalized link suggestions to the users visiting a Web site. In particular, a neuro-fuzzy network is trained using information about user categories to discover a set of fuzzy rules capturing the associations between user behavior models and relevance degrees of pages to be recommended. A comparison with other recommendation approaches shows the effectiveness of the proposed neuro-fuzzy approach in finding good recommendation rules.

Published in:

Hybrid Intelligent Systems, 2008. HIS '08. Eighth International Conference on

Date of Conference:

10-12 Sept. 2008