Product Personalized Recommendation Engine Based on Collaborative Filtering Algorithm

Abstract

Nowadays, information plus Internet technology is continuously developing, and people have entered the era of information overload from the era of lack of information access. In this context, it is difficult to find one's own "tailor-made" information from the huge amount of information. In this context, the recommendation system came into being. This article uses the order data under an e-commerce platform, uses user-based and item-based collaborative filtering algorithms to personalize products, and builds a product recommendation system. Through the feasibility of actual application scenarios and the prediction score of the model, a series of evaluations were made on the algorithm, and reliable and effective prediction results were obtained from the three indicators of accuracy, recall and F1 score. The final recommendation effect of the TopN recommendation list obtained was significant, and the recommendation efficiency could reach 85%.Provide some ideas for the research and development direction of modern recommendation algorithms.