Push notification is a critical recommendation scenario on large-scale platforms, allowing the system to proactively reach users outside the application to improve long-term re-engagement. However, designing an optimal push system requires handling a complex action space for the"whether and when"delivery problem under strict system resource constraints. Existing solutions typically fall into two passive paradigms: pre-planned frequency methods that allocate delivery times via offline modeling, limiting real-time adaptability; and fixed-interval triggering methods that periodically poll the system, creating a strict dilemma between excessive computational overhead and diminished optimal timing capture. Furthermore, such multi-stage frameworks severely suffer from local optima. To overcome these limitations, in this paper, we propose STEPS, a proactive, Self-Triggered End-to-end Agentic Push Recommendation System, which is already fully deployed at Douyin with over 1 billion users. STEPS reformulates push recommendation as a self-triggered agentic process in which the system decides not only whether to send a push, but also when to invoke itself again, thereby forming a closed loop that balances real-time effectiveness and efficiency. Specifically, STEPS consists of two decision transformer-based agents: a planning agent that schedules the next system invocation using a gated ordinal regression method, and an execution agent that decides whether to send a push based on trajectory rewards. Furthermore, we introduce a lightweight filtering agent to both control computational overhead and act as a crucial safeguard against unreasonable planning behaviors. Online A/B testing demonstrates that STEPS significantly increases user active days by 0.2843% and reduces the push permission disablement rate by 1.9089%, while the filtering agent reduces computational overhead by 79.42%.
Zhao-Yu Zhang, Qingying Chen, Chunyuan Zheng et al.· 0 citations
In marketing, optimizing subsidy allocation to maximize overall profits is of substantial economic importance. Prior research has employed treatment effect estimation techniques to identify subsidy-sensitive items and design corresponding allocation strategies. However, more accurate treatment effect estimations do not necessarily lead to better allocations, underscoring the critical influence of decision boundaries in decision-making. This paper argues that optimal allocation fundamentally depends on predicting the expected optimal subsidy, a challenge distinct from conventional treatment effect estimation or causal decision-making, which existing approaches fail to address. To fill this gap, we introduce a two-stage Counterfactual optimal subsidy Learning method with an Asymmetric reward (CoLA). In the first stage, we derive a coarse estimate of the expected subsidy threshold by exploiting order information and the conditional independence between expected and observed subsidies. In the second stage, we refine these estimates using an asymmetric loss function, leading to more robust predictions. Under practical budget constraints, we prioritize candidates based on their Sharpe ratios to determine the final subsidy allocation strategy. Experiments on three public datasets and an online A/B test show that our method achieves significant performance improvements, yielding the highest total profit and incremental leverage ratios.
Xiang Li, Yanghao Xiao, Chunyuan Zheng et al.· Annual International ACM SIG...· 2 citations
Collected data with non-random missing labels poses a widely recognized challenge for unbiased learning. For example, in recommender systems, users are free to choose whether or not to rate an item. To achieve unbiased learning under MNAR data, a variety of methods have been proposed, such as reweighting and imputation. Among them, doubly robust (DR) based methods are widely adopted due to their appealing theoretical guarantees. However, these guarantees rely on strong assumptions that either the propensity or the imputation is accurate for all units (such as user-item pairs), which is very hard to achieve in real-world scenarios. Previous studies show that a small error in imputation can lead to a large bias in DR-based methods. Furthermore, for units with missing labels, we lack an effective method to evaluate the imputation quality. In this work, we propose a model-agnostic framework to assess the accuracy of imputed labels and to correct imputations with large bias based on conformal prediction. Specifically, we leverage conformal prediction to construct a valid prediction set for units with unobserved labels, and revise imputations that fall outside this set. Extensive experiments are conducted on three real-world datasets and one semi-synthetic dataset to show the effectiveness of our proposed method. Our code is available at https://github.com/lixiang-222/conformal-prediction-for-MNAR.
Chunyuan Zheng, Xiang Li, Hang Pan et al.· Proceedings of the 32nd ACM...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
