Design of online retail commodity recommendation system based on Item-CF algorithm
Ge Gao · 2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) · 2021
With the rapid development of e-commerce, more and more consumers conduct consumption behavior through various online shopping platforms. The traditional e-commerce platform recommendation system is difficult to achieve more accurate matching according to the user input data. In view of this situation, this time through the personalized and diversified optimization and improvement of the traditional item algorithm, and on this basis, a commodity recommendation system is designed. In order to verify the comprehensive performance of the recommended algorithm, by comparing the performance indexes of the improved Item-CF algorithm with the other three algorithms, it can be seen that Item-CF has better accuracy, calling rate, coverage and diversity.