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.
Zhao-Yu Zhang, Qing-Ying Chen, Chun-Yuan Zheng et al.· Proceedings of the 20th ACM...· 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...
Xiang Li, Yanghao Xiao, Chun-Yuan Zheng et al.· Annual International ACM SIG...· 2 citations
This work proposes a model-agnostic framework to assess the accuracy of imputed labels and to correct imputations with large bias based on conformal prediction, and leverage conformal prediction to construct a valid prediction set for units with unobserved labels, and revise imputations that fall outside this set.
Chun-Yuan Zheng, Xiang Li, Hang Pan et al.· Proceedings of the 32nd ACM...· 0 citations
This study theoretically shows that when zero-propensity samples exist, both IPS and DR estimators become biased, and proposes ExtraDebias method, which enables debiased recommendation in both non-extrapolation and extrapolation samples.
Yanghao Xiao, Hao Wang, Xiang Li et al.· Annual International ACM SIG...· 1 citation
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