E-commerce Webpage Recommendation Scheme Base on Semantic Mining and Neural Networks

TL;DR

This paper introduces a novel e-commerce webpage recommendation method combining semantic web mining and BP neural networks, improving accuracy and speed in identifying user-relevant pages.

Contribution

It proposes a new recommendation scheme that integrates semantic features and neural network classification to enhance e-commerce webpage suggestions.

Findings

High accuracy in webpage identification

Fast recommendation processing

Effective use of semantic features

Abstract

In e-commerce websites, web mining web page recommendation technology has been widely used. However, recommendation solutions often cannot meet the actual application needs of online shopping users. To address this problem, this paper proposes an e-commerce web page recommendation solution that combines semantic web mining and BP neural networks. First, the web logs of user searches are processed, and 5 features are extracted: content priority, time consumption priority, online shopping users' explicit/implicit feedback on the website, recommendation semantics and input deviation amount. Then, these features are used as input features of the BP neural network to classify and identify the priority of the final output web page. Finally, the web pages are sorted according to priority and recommended to users. This project uses book sales webpages as samples for experiments. The results show that this solution can quickly and accurately identify the webpages required by users.