Open AccessPrediction of Internet User’s Purchase Behavior Based on Mixed kernel SVM Model
Author(s) -
Wen Hu,
Yuxue Shi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1769/1/012007
Subject(s) - support vector machine , computer science , particle swarm optimization , feature selection , artificial intelligence , machine learning , kernel (algebra) , ranking (information retrieval) , selection (genetic algorithm) , data mining , feature (linguistics) , mathematics , combinatorics , linguistics , philosophy
In order to better predict the purchase behavior of online consumers and improve the purchase conversion rate of e-commerce companies, a model of consumer purchase intention prediction based on mixed kernel function support vector machine is proposed. In the research, the prediction model of consumer purchase intention is designed as a two-classification problem, and the feature selection algorithm mRMR is used to obtain the ranking of features, seeking to obtain better or similar classification results when choosing fewer features. On its basis, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of the model. Experiments show that, to a certain extent, the constructed mixed kernel function can effectively improve the classification effect of SVM.