Title: Research on Weibo marketing advertising push method based on social network data mining

Authors: Yanyan Zhang

Addresses: Anqing Vocational and Technical College, Anqing, 246003, Anhui, China

Abstract: The current advertising push methods have low accuracy and poor advertising conversion effects. Therefore, a Weibo marketing advertising push method based on social network data mining is studied. Firstly, establish a social network graph and use graph clustering algorithm to mine the association relationships of users in the network. Secondly, through sparsisation processing, the association between nodes in the social network graph is excavated. Then, evaluate the tightness between user preferences and other nodes in the social network, and use the TF-IDF algorithm to extract user interest features. Finally, an attention mechanism is introduced to improve the deep learning model, which matches user interests with advertising domain features and outputs push results. The experimental results show that the push accuracy of this method is higher than 95%, with a maximum advertising click through rate of 82.7% and a maximum advertising conversion rate of 60.7%.

Keywords: social network data; data mining; Weibo marketing advertising; advertising push; graph clustering; attention mechanism.

DOI: 10.1504/IJEB.2024.141855

International Journal of Electronic Business, 2024 Vol.19 No.4, pp.393 - 406

Received: 30 Aug 2023
Accepted: 05 Dec 2023
Published online: 02 Oct 2024 *

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