A multi-agent recommender system for supporting device adaptivity in e-Commerce

Abstract

Traditional recommender systems for e-Commerce support the customers' activities providing them with useful suggestions about available products in Web stores. To this purpose, in an agent-based context, each customer is often associated with a customer agent that interacts with the site agent associated with the visited e-Commerce Web site. In presence of a high number of interactions between customers and Web sites, the generation of recommendations can be a heavy task for both these agents. Moreover, customers can navigate on the Web by using different devices having different characteristics that may influence customer's preferences. In this paper we propose a new multi-agent system, called ARSEC, where each device exploited by a customer is associated with a device agent that autonomously monitors his/her behaviour. Furthermore, each customer is associated with a customer agent that collects in a global profile the information provided by his/her device agents and each e-Commerce Web site is associated with a seller agent. Based on the similarity existing among the global profiles the customers are partitioned in clusters, each one managed by a counsellor agent. Recommendations are generated in ARSEC as result of the collaboration between the seller agent and some counsellor agents associated with the customer. The usage of the device agents leads to generating recommendations taking into account the device currently used, while the fully decentralized architecture introduces a strong reduction of the time costs. Some experimental results are presented to show the significant advantages obtained by ARSEC in terms of recommendation effectiveness with respect to other well-known agent-based recommenders.