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

arXiv: https://arxiv.org/abs/2409.07033 · HTML: https://mlsanity.com/paper/2409.07033/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation](https://mlsanity.com/paper/2306.04811/) (2023)
- [Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2310.04148/) (2023)
- [Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation](https://mlsanity.com/paper/2308.09917/) (2023)
- [Web Mining Techniques in E-Commerce Applications](https://mlsanity.com/paper/1311.7388/) (2013)

## Built on by (most cited first, 1 of 1)

- [In-Browser Agents for Search Assistance](https://mlsanity.com/paper/2601.09928/) (2026)

## Abstract (arXiv)

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.