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Found 3 papers, 1 papers with code
Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing
ECUP consists of two primary components: 1) the Entire Chain-Enhanced Network, which utilizes user behavior patterns to estimate ITE throughout the entire chain space, models the various impacts of treatments on each task, and integrates task prior information to enhance context awareness across all stages, capturing the impact of treatment on different tasks, and 2) the Treatment-Enhanced Network, which facilitates fine-grained treatment modeling through bit-level feature interactions, thereby enabling adaptive feature adjustment.
Probabilistic Prediction of Longitudinal Trajectory Considering Driving Heterogeneity with Interpretability
Specifically, based on a certain length of historical trajectory data, the situation-specific driving preferences of each driver are identified, where key driving behavior feature vectors are extracted to characterize heterogeneity in driving behavior among different drivers.
A Deep Behavior Path Matching Network for Click-Through Rate Prediction
User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making.
