Article: Customer interest classification method of e-commerce trading platform based on decision tree algorithm Journal: International Journal of Networking and Virtual Organisations (IJNVO) 2023 Vol.29 No.3/4 pp.419 - 434 Abstract: Due to the large number of customers and diverse interest characteristics in e-commerce trading platforms, there are many problems such as large classification errors, low accuracy in customer interest feature extraction, and high classification time cost. A decision tree algorithm-based customer interest classification method for e-commerce trading platforms is proposed. Based on the basic structure of e-commerce transaction platform web pages, non-target nodes and target nodes are removed, and the DFSD fusion method is introduced to extract web browsing content. Then, multi-dimensionally annotate the key interest information and extract customer interest features through multimodal feature fusion. Build a customer interest tree for e-commerce trading platforms, with the customer interest feature data being processed by calculating its entropy value, calculating the information gain of leaf nodes, and building a decision tree classification model based on specific classification rules. Experimental results show that this method reduces classification errors and has good classification results. Inderscience Publishers - linking academia, business and industry through research

Title: Customer interest classification method of e-commerce trading platform based on decision tree algorithm

Authors: Xiaowei Ma; Xin Yao; Shizhong Guo

Addresses: Department of Economics and Management, Hebei Energy College of Vocation and Technology, Tangshan, 063000, China ' Department of Information Engineering, Hebei Energy College of Vocation and Technology, Tangshan, 063000, China ' Department of Transportation, Tangshan Maritime Vocational College, Tangshan, 063000, China

Abstract: Due to the large number of customers and diverse interest characteristics in e-commerce trading platforms, there are many problems such as large classification errors, low accuracy in customer interest feature extraction, and high classification time cost. A decision tree algorithm-based customer interest classification method for e-commerce trading platforms is proposed. Based on the basic structure of e-commerce transaction platform web pages, non-target nodes and target nodes are removed, and the DFSD fusion method is introduced to extract web browsing content. Then, multi-dimensionally annotate the key interest information and extract customer interest features through multimodal feature fusion. Build a customer interest tree for e-commerce trading platforms, with the customer interest feature data being processed by calculating its entropy value, calculating the information gain of leaf nodes, and building a decision tree classification model based on specific classification rules. Experimental results show that this method reduces classification errors and has good classification results.

Keywords: decision tree algorithm; e-commerce trading platform; customer interest classification; DFSD fusion method; entropy value; information gain.

DOI: 10.1504/IJNVO.2023.135955

International Journal of Networking and Virtual Organisations, 2023 Vol.29 No.3/4, pp.419 - 434

Received: 21 Mar 2023
Accepted: 06 Aug 2023
Published online: 10 Jan 2024 *

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Title: Customer interest classification method of e-commerce trading platform based on decision tree algorithm

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