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

Subject Areas : Computer Engineering

Ramazan Teimouri Yansari ¹ (Department of Computer Engineering, Bandar Gaz Branch, Islamic Azad University, Bandar Gaz, Iran)
Mojtaba Ajoudani ² (Department of Electrical Engineering, Bandar Gaz Branch, Islamic Azad University, Bandar Gaz, Iran)
Seyed Reza Mosayyebi ³ (Department of Electrical Engineering, Bandar Gaz Branch, Islamic Azad University, Bandar Gaz, Iran)

Received: 2023-08-11 Accepted : 2023-11-13 Published : 2023-12-01

Keywords: Machine Learning, Recommender Systems, Intelligent agents, Computer-aided System, Multi-agent systems (MAS),

Abstract :

Abstract – Due to the increasing amount of information and services available on the web, it is necessary to provide tools such as recommender systems to websites and applications that can help users find information and services that suit their interests. For this reason, 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 are information systems that help in the decision-making process by modeling the behavior of users in operational environments in ranking, comparing, selecting and preferring user items, by limiting the search space through high-quality and accurate recommendations. In this research, a multi-agent recommender system was proposed 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 including 1067371 records of online sales data has been used. According to the results, in this evaluation, the accuracy of the proposed model was 91.5% on average. 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.

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