System Development & Application
|
2422-2426

Click prediction model based on product description

Huang Haoxuan
Sheng Wu
College of Economics & Management, Anhui University of Science & Technology, Huainan Anhui 232001, China

Abstract

In order to predict the impact of commodity characteristics on click in commodity description copy, quantitatively analyze users' consumption behavior characteristics and alleviate the cold start problem, this paper established a click prediction model based on LDA model and text emotion analysis. By means of the classification and screening of the commodity description words by the LDA topic model, the model analyzed the emotion of the constituent words, constructed the feature vector to represent the user's emotional tendency to the characteristics of the commodity, and predicted the click through the LightGBM algorithm. The model transformed unstructured text data into structured data, quantified users' interest in different characteristics of goods, and used the similar characteristics of different goods to alleviate the cold start problem. The experimental results show that the model can effectively improve the click prediction effect and alleviate the cold start problem.

Foundation Support

安徽省自然科学基金资助项目(1808085MG212)
安徽省高等学校省级教学示范课基金资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.01.0025
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 8
Section: System Development & Application
Pages: 2422-2426
Serial Number: 1001-3695(2022)08-030-2422-05

Publish History

[2022-03-25] Accepted Paper
[2022-08-05] Printed Article

Cite This Article

黄皓炫, 盛武. 基于商品描述文案的点击预测模型 [J]. 计算机应用研究, 2022, 39 (8): 2422-2426. (Huang Haoxuan, Sheng Wu. Click prediction model based on product description [J]. Application Research of Computers, 2022, 39 (8): 2422-2426. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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