Authors:
Marco Degemmis
;
Pasquale Lops
;
Giovanni Semeraro
;
M. Francesca Costabile
;
Oriana Licchelli
and
Stefano P. Guida
Affiliation:
University of Bari, Italy
Keyword(s):
Hybrid recommender systems, Information Filtering, Machine learning, User profiling
Related
Ontology
Subjects/Areas/Topics:
B2B, B2C and C2C
;
B2C/B2B Considerations
;
Business and Social Applications
;
Case Studies
;
Communication and Software Technologies and Architectures
;
e-Business
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Internet and Collaborative Computing
;
Neural Rehabilitation
;
Neurotechnology, Electronics and Informatics
;
Simulation and Modeling
;
Simulation Tools and Platforms
;
Society, e-Business and e-Government
;
Software Agents and Internet Computing
;
Web Information Systems and Technologies
Abstract:
Nowadays, users are overwhelmed by the abundant amount of information delivered through the Internet. Especially in the e-commerce area, largest catalogues offer millions of products and are visited by users having a variety of interests. It is of particular interest to provide customers with personal advice: Web personalization has become an indispensable part of e-commerce. One type of personalization that many Web sites have started to embody is represented by recommender systems, which provide customers with personalized advices about products or services. Collaborative systems actually represent the state-of-the-art of recommendation engines used in most e-commerce sites. In this paper, we propose a hybrid method that aims at improving collaborative techniques by means of user profiles that store knowledge about user interests.