After entering the 21st century, the electronic commerce system has affected all aspects of our lives. Whether we read news on our mobile phones or computers or purchase items on our online websites, it greatly facilitates our lives. With the rapid development of short videos, many people like to watch small videos that interest them. The rapid development of e-commerce has facilitated our lives, so that we no longer have to go to many shopping malls to buy our favorite items, and we also no need to change TV stations one by one after watching a program to find our favorite programs. However, due to the rapid development of electronic commerce, there has been a lot of information overload. When users browse the website, items they are not interested in will appear, and even information about online fraud appears. How to filter this information and how to intelligently recommend to users more favorite items is the main research direction of this article. The research of this article is mainly divided into four parts. The first part analyzes the current situation of intelligent recommendation technology research and puts forward the idea of this article. The second part introduces the commonly used collaborative filtering algorithm and the principle and process of the fuzzy clustering algorithm used in this experiment, analyzes the shortcomings of the traditional collaborative filtering algorithm and illustrates the adaptability of the fuzzy clustering algorithm in practical applications. The third part introduces an intelligent recommendation system based on fuzzy clustering, which comprehensively analyzes the characteristics of users and products, makes full use of users’ evaluation information of products, and realizes intelligent recommendations based on content and collaborative filtering. At the end of the article, the comparative analysis experiment with the intelligent recommendation system of collaborative recommendation algorithm further proves the superiority of the intelligent recommendation system of electronic commerce based on fuzzy clustering algorithm in this paper and improves the accuracy of intelligent recommendation.
A Fuzzy Clustering Based Collaborative Filtering Algorithm for Time-aware POI Recommendation
