Item-Based Filtering and Semantic Networks for Personalized Web Content Adaptation in E-Commerce

Authors:
Panayiotis Koutsabasis;John Darzentas
Affiliations:
Department of Product and Systems Design Engineering, University of the Aegean, Hermoupolis, Greece GR-84100;Department of Product and Systems Design Engineering, University of the Aegean, Hermoupolis, Greece GR-84100
Venue:
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Year:
2008

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Abstract

Personalised web content adaptation systems are critical constituents of successful e-commerce applications. These systems aim at the automatic identification, composition and presentation of content to users based on a model about their preferences and the context of interaction. The paper critically reviews related work in the field and presents an integrated approach for the design of personalization and web content adaptation in e-commerce that places emphasis on item-based collaborative filtering and on short-term, dynamic user models represented as semantic networks. The proposed approach for personalised web content adaptation can provide different types of interesting recommendations taking into account the current user interaction context in a computationally inexpensive way. It is also respectful of user personal information and unobtrusive with respect to user feedback.