Yang Liu
ABSTRACT
With the progress of the Internet information age and the rapid development of Internet technology, the number of users of ECP( e-commerce platforms) has increased dramatically, and the amount of information on these platforms has also shown a considerable increase. In the above context, user recommendation algorithms on ECP have become very popular research content and product recommendation technology. The product recommendation technology has also been widely used to filter product information actively. Among many recommendation algorithms, the most important application is the clustering nearest neighbor collaboration algorithm, and this algorithm has the advantages of simple and efficient filtering, so it has received widespread attention from researchers at home and abroad. This paper proposes an e-commerce recommendation algorithm based on data clustering based on the above background. The recommendation algorithm mainly uses the nearest-neighbor collaborative filtering algorithm to improve the algorithm's quality. This article first summarizes the mainstream domestic recommendation algorithms. On this basis, a processing plan for e-commerce collaborative filtering data is proposed, and then an ECP recommendation algorithm based on mean clustering is designed.
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