Research on Collaborative Filtering of Food Information Security in E-Commerce Platform

Abstract

Recommender systems are one of the most important technologies in the electronic commerce system. In a collaborative filtering recommendation algorithm, similarity calculation is the key to determining the efficiency of the recommendation algorithm. This paper analyzes the shortcomings of traditional similarity measurement methods in recommender systems and proposes a scoring-matrix-filling algorithm. Based on information categories and user interest similarity, the algorithm can reduce the negative influence of data sparsity on the recommendation result to some extent. The research results have certain practical and guiding significance.