Key points are not available for this paper at this time.
E-commerce has been overgrown due to the ongoing advancements in Internet Technology and the notable enhancement of network fundamentals. Nowadays, the most popular type of electronic commerce is online shopping. These days, user profile-based online recommender systems are popular in engineering and research fields. Getting users' profiles right is essential for making recommendations. Many studies on extracting user profiles, including content-based recommendations, have been initiated recently. We have introduced an intelligent recommendation system for e-commerce, aiming to streamline online shopping by addressing information overload. This system leverages customer behaviour to tailor personalized suggestions and enhance user experiences. It combines a genetic algorithm and a decision tree model for e-commerce platforms. The goal is to smooth the shopping journey, ensuring users find what they seek amidst the vast online options. This approach prioritizes user preferences, reducing the risk of overlooking crucial details when navigating online stores. Through experiments and analysis, we assess its effectiveness compared to conventional recommendation systems. We tested the model using publicly available datasets, comparing its performance against existing recommendation systems. The suggested approach is targeted to decrease recommendation sparsity and increase recommendation accuracy.
Deshmukh et al. (Mon,) studied this question.