Multi-Agent Personalized Recommendation System in E-Commerce based on User

Abstract

As more sectors began to switch from conventional business models to e-commerce in response to the general trend toward mobile Internet use, the scale of e-commerce grew rapidly. There are three types of recommendation systems: hybrid, collaborative, content-based. Content based systems take into consideration the characteristics of the recommended objects. Then, titles in the database that have been classified as “romantic” are selected using a content-based recommendation method. Collaborative filtering systems utilize similarity measures to recommend items that are shared by individuals or objects with similar interests. Users are recommended items based on their preferences. In the recommendation system, collaborative filtering is the most popular and effective suggestion process. However, system performance impact as the amount of time required to locate the target user's closest neighbor across the entire user space increases with the number of users and products in the e-commerce system. The applied and designed Multi-Agent personalized recommendation system in E-commerce can be analyzed using user clustering in the Multi-Agent to E-commerce personalized recommendation system. An implementation strategy for recommendations based on user clustering is shown in this analysis. According to their scores for commodity categories, users are clustered, and only the nearest neighbours in their categories are searched, so that as many nearest neighbors as possible can be searched. The accuracy, recall, and specificity of this analysis are used to calculate its performance. In this analysis the presented method will give better results.