Using Support Vector Machines for feature-oriented profile-based recommendations
by Angel Garcia-Crespo, Juan Miguel Gomez-Berbis, Ricardo Colomo-Palacios, Francisco Garcia-Sanchez
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 1, No. 4, 2009

Abstract: Recommendations have reached a new dimension in a ubiquitous computing environment. A vast amount of information is coming from pervasive services and devices, providing a potential source of value-added knowledge. However, this knowledge must be exposed through a classification technique such as Support Vector Machines (SVMs), which would allow categorising, classifying and evaluating this information based on features and profiles to foster the efficiency of recommendations of informational items. This paper presents an algorithm based on SVMs and an underlying architecture to extract and build a number of recommendations based on features and profile preferences from the user.

Online publication date: Thu, 25-Jun-2009

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email