Title: Analysis model of the short-term search behaviour guidance of e-commerce platform users based on knowledge graph

Authors: Bin Li; Zhisheng Zhou

Addresses: Institute of Economics and Management, Jiangxi Teachers College, YingTan, 335000, China ' Institute of Economics and Management, Jiangxi Teachers College, YingTan, 335000, China

Abstract: In order to solve the problems of poor satisfaction with product guidance and low user order rate in existing e-commerce platforms, a knowledge graph based short-term search behaviour guidance analysis model for e-commerce platform users is proposed. Firstly, construct a knowledge graph to collect samples of users' short-term search behaviour. Then, attribute preference weighting is applied to users' short-term search behaviour, and the optimal sequential search theory is introduced to construct a user short-term search behaviour guidance analysis model. Finally, matrix decomposition method is used to extract the features of users and products, achieving short-term search behaviour guidance analysis for users. The results show that after using the guidance analysis method in this article, user satisfaction with the product and user order rate can always reach over 90%, indicating good application performance.

Keywords: knowledge graph; optimal sequential search strategy; attribute preference weighting; search behaviour; matrix decomposition.

DOI: 10.1504/IJNVO.2023.135953

International Journal of Networking and Virtual Organisations, 2023 Vol.29 No.3/4, pp.285 - 298

Received: 21 Feb 2023
Accepted: 12 Jun 2023
Published online: 10 Jan 2024 *

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