Design an Intelligent Multi-agent Computer-aided Model for Recommender Systems

Increasing the information and services available on the web, providing tools such as recommender systems to websites and applications for users to find information and services according to their interests, seems necessary.Therefore, providing appropriate guidance and suggestions to users in different choices, according to the user's priorities, has found a special position in different fields. Recommender systems proactively recommends items that usersmay prefer. We proposed, a multi-agent recommender system that can provide suitable recommendations as a shopping assistant in the purchasing process. To analyze the proposed model, the sales dataset of an online store has been used.According to the results, in this evaluation, the accuracy of the proposed model was better than common models such as Naïve Bayesian and artificial neural networks.By combining multi-agent systems, multi-agent recommender systems were proposed that can provide suitable recommendations as a purchasing assistant in the purchasing process. The results of applying the proposed model on the data related to the purchase history of the customers of an online shopping showed that the proposed model has a good efficiency in evaluating the parameters used in comparison with the common methods in this property field.