Title: Electronic commerce information personalised recommendation method based on social network data mining

Authors: Ping Wen; Ding Ding

Addresses: Academic Affairs Office, Anhui Electrical Engineering Vocational and Technical College, Hefei, 230051, China ' Audit Department, AnHui Audit College, Hefei, 230601, China

Abstract: In order to improve the accuracy of e-commerce platform recommendations for users and shorten the recommendation time, this study conducted a personalised recommendation method for e-commerce information based on social network data mining. First, use the crawler algorithm to complete social data mining, and extract data features to build user profiles. Then, use the collaborative filtering algorithm to complete the extraction of user preference features and use the Pearson algorithm to calculate the similarity between the products that users browse for a long time and the user preference features. Finally, complete the matching calculation between the preference features and the information about the products to be recommended, and then complete the output of the recommendation set. The experiment proves the progressiveness of the proposed method. The results show that the recommendation accuracy of this method is higher than 90%, and the recommendation time is within 15 s. This method can effectively improve the recommendation accuracy and reduce the time cost of recommendation, and has great application value.

Keywords: data mining; electronic commerce; personalised recommendation; user portrait; preference features.

DOI: 10.1504/IJNVO.2023.135950

International Journal of Networking and Virtual Organisations, 2023 Vol.29 No.3/4, pp.299 - 311

Received: 13 Mar 2023
Accepted: 13 Jun 2023
Published online: 10 Jan 2024 *

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