User behavior prediction based on fusion model

Yangyang Zhong; Wenhao Li

doi:10.1117/12.2645793

23 August 2022 User behavior prediction based on fusion model

Yangyang Zhong, Wenhao Li

Proceedings Volume 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022); 123050X (2022) https://doi.org/10.1117/12.2645793
Event: International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 2022, Hangzhou, China

Abstract

After the emergence of e-commerce platforms and the rapid growth of users, the growth of domestic e-commerce traffic has gradually peaked. It is known that improving flow conversion is an important and urgent issue. This paper mainly constructs an accurate user purchase prediction algorithm in two aspects: Feature Engineering and model construction Xgboost is introduced to construct an iterative lifting tree for the original features, measure the importance of the features, and select the features to reduce the complexity of the model solution and improve the prediction accuracy; In terms of model building, the multi-model fusion method based on stacking is used to design a two-layer fusion model. The first layer uses xgboost and lightgbm as basic learners to predict users' purchases from different angles. The second layer uses logical regression to fuse the prediction results of the basic model with meta learners to obtain more accurate purchase predictions. The experimental results show that the accuracy and stability of the model are better than the benchmark model. Accurate purchase prediction helps improve the user experience and marketing promotion effect to improve the traffic conversion rate.

Citation Download Citation

Yangyang Zhong and Wenhao Li "User behavior prediction based on fusion model", Proc. SPIE 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 123050X (23 August 2022); https://doi.org/10.1117/12.2645793

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