融合语义挖掘与神经网络，精准推荐电商页面。
E-commerce Webpage Recommendation Scheme Base on Semantic Mining and Neural Networks
- 从用户行为中提取5类特征，输入BP神经网络分类
- 实验表明能快速准确识别用户所需网页
- 适合需要个性化推荐的电商平台使用
在电商网站中，网页推荐技术已被广泛应用。然而，现有推荐方案往往难以满足在线购物用户的实际需求。为此，本文提出一种结合语义网页挖掘与BP神经网络的电商网页推荐方案。首先，处理用户搜索日志，提取5个特征：内容优先级、耗时优先级、用户对网站的显式/隐式反馈、推荐语义及输入偏差量。随后，将这些特征作为BP神经网络的输入，用于分类并识别最终输出网页的优先级。最后，根据优先级对网页排序并推荐给用户。本项目以图书销售网页为样本进行实验，结果表明该方案能快速且准确地识别用户所需的网页。
原文摘要 · Abstract (English)
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.
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