Open AccessAn improved deep forest model for prediction of e-commerce consumers’ repurchase behavior
Author(s) -
Weiwei Zhang,
Mingyan Wang
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0255906
Subject(s) - computer science , feature (linguistics) , construct (python library) , the internet , consumer behaviour , set (abstract data type) , product (mathematics) , feature engineering , e commerce , deep learning , machine learning , data mining , marketing , business , world wide web , philosophy , linguistics , geometry , mathematics , programming language
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.