TRES: A Decentralized Agent-Based Recommender System to Support B2C Activities
The increasing relevance assumed by the E-Commerce in the Web community is attested by the great number of powerful and sophisticated tools developed in the last years to support traders in their commercial activities. In this scenario, recommender systems appear doubtless as a promising solution for supporting both customers’ and merchants’ activities. In this paper, we propose an agent-based recommender system, called TRES, able to help traders in Business-to-Consumer activities with useful and personalized suggestions based on interests and preferences stored in customers’ profiles, adopting a fully decentralized architecture that suitably introduces in the system both scalability and privacy protection.
KeywordsInterest Rate Recommender System Product Category Recommendation Algorithm Product Instance
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