ArticlePloS one2021
An improved deep forest model for prediction of e-commerce consumers' repurchase behavior.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Use of Machine Learning Techniques for Fertilization Traceability Discrimination via Core Quality Indicators of Korla Fragrant Pear Fruits.Foods (Basel, Switzerland) · 2026Article
- RETRACTED: Enhanced E-commerce decision-making through sentiment analysis using machine learning-based approaches and IoT.PloS one · 2025Article
- Sustainable customer retention through social media marketing activities using hybrid SEM-neural network approach.PloS one · 2022Article
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Authors and funding
2 authors.
Funding
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Abstract
As the Internet retail industry continues to rise, more and more consumers choose to shop online, especially Chinese consumers. Using consumer behavior data left on the Internet to predict repurchase behavior is of great significance for companies to achieve precision marketing. This paper proposes an improved deep forest model, and the interactive behavior characteristics of users and goods are added into the original feature model to predict the repurchase behavior of e-commerce consumers. Based on the Alibaba mobile e-commerce platform data set, first construct a feature engineering that includes user characteristics, product characteristics, and interactive behavior characteristics. And then use our proposed model to make predictions. Experiments show that the model's overall performance with increased interactive behavior features is better and has higher accuracy. Compared with the existing prediction models, the improved deep forest model has certain advantages, which not only improves the prediction accuracy but also reduces the cost of training time.
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