Design of Hybrid Recommendation Algorithm in Online Shopping System

Abstract

In order to improve user satisfaction and loyalty on e-commerce websites, recommendation algorithms are used to recommend products that may be of interest to users. Therefore, the accuracy of the recommendation algorithm is a primary issue. So far, there are three mainstream recommendation algorithms, content-based recommendation algorithms, collaborative filtering algorithms and hybrid recommendation algorithms. Content-based recommendation algorithms and collaborative filtering algorithms have their own shortcomings. The contentbased recommendation algorithm has the problem of the diversity of recommended items, while the collaborative filtering algorithm has the problem of data sparsity and scalability. On the basis of these two algorithms, the hybrid recommendation algorithm learns from each other’s strengths and combines the advantages of the two algorithms to provide people with better services. This article will focus on the use of a content-based recommendation algorithm to mine the user’s existing interests, and then combine the collaborative filtering algorithm to establish a potential interest model, mix the existing and potential interests, and calculate with the candidate search content set. The similarity gets the recommendation list.