Cookie cats pop game9/22/2023 Extensive experiments on a real-world Tencent QQ browser dataset Furthermore, a Tree-based classifier is attached for churn prediction instead of using the multilayer perceptron. To meet this challenge, we propose a novel model named Multivariate Behavior Sequence Transformer (MBST) with two complementary attention mechanisms to explore the temporal and behavioral information separately. However, traditional churn prediction algorithms such as Tree-based models cannot exploit the temporal characteristics of browser customers behaviors, while sequence models cannot explicitly extract the information between multiple behaviors. Churn prediction based on customer behaviors plays a vital role in customer retention strategies. In the competitive web browser market, identifying potential churners is critical to decreasing the loss of existing customers.
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