Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
X-Compass: An XML Agent for Supporting User Navigation on the Web
FQAS '02 Proceedings of the 5th International Conference on Flexible Query Answering Systems
A User Behavior-Based Agent for Improving Web Usage
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
An Adaptive e-Commerce System Definition
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
MASHA: A multi-agent system handling user and device adaptivity of Web sites
User Modeling and User-Adapted Interaction
Adaptive web navigation for wireless devices
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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Agent-based Web recommender systems are applications capable to generate useful suggestions for visitors of Web sites. This task is generally carried out by exploiting the interaction between two agents, one that supports the human user and the other that manages the Web site. However, in the case of large agent communities and in presence of a high number of Web sites these tasks are often too heavy for the agents, even more if they run on devices having limited resources. In order to address this issue, we propose a new multi-agent architecture, called MARS, where each user's device is provided with a device agent, that autonomously collects information about the local user's behaviour. A single profile agent, associated with the user, periodically collects such information coming from the different user's devices to construct a global user profile. In order to generate recommendations, the recommender agent autonomously pre-computes data provided by the profile agents. This recommendation process is performed with the contribution of a site agent which indicates the recommendations to device agents that visit the Web site. This way, the site agent has the only task of suitably presenting the site content. We performed an experimental campaign on real data that shows the system works more effectively and more efficiently than other well-known agent-based recommenders.