Reliable policy optimization in real-time bidding (RTB) demands a high-fidelity world model capable of simulating stochastic market dynamics under constraints. However, current generative simulators are limited by their reliance on deterministic point prediction objectives (e.g., mean squared error). These approaches are statistically ill-posed for auction data, failing to capture the extreme heteroscedasticity where variance scales explosively with the mean, and neglecting the structural coupling between feedback variables. In this paper, we bridge this gap through a physics-informed statistical modeling framework derived from first principles. Axiomatically, we substantiate that the marginal distributions of bidding feedback follow Poisson-lognormal and Tweedie-lognormal laws, and operationalize them into an efficient zero-inflated generalized beta of the second kind (ZI-GB2) surrogate. This physical formulation explicitly models variance scaling, naturally resolving the gradient dominance issues inherent in heteroscedastic regression. Furthermore, we identify that the asymptotic tail dependence between cost and value arises from the shared underlying winning events, necessitating a normalizing flow copula to capture these non-Gaussian co-movements. Extensive experiments on large-scale production datasets demonstrate that our framework achieves state-of-the-art distributional fidelity and exhibits clear neural scaling laws, establishing a rigorous probabilistic foundation for future constraint-aware stochastic control.
Chenyang Wu, Tianyu Wang, Shengjun Fang et al.· Proceedings of the 32nd ACM...· 0 citations
Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, most industrial systems remain heavily reliant on historical co-occurrence patterns and log-fitting objectives, i.e., optimizing for past user interactions without explicitly modeling user intent. This log-fitting approach often leads to overfitting to narrow historical preferences, failing to capture users’ potential interests. As a result, it reinforces filter bubbles and long-tail phenomena, ultimately harming user experience and threatening the sustainability of the recommendation ecosystem. To address these challenges, we rethink the overall design paradigm of recommender systems and propose RecGPT, a fully integrated, production-ready framework that places user intent at the center of the recommendation pipeline. By integrating large language models (LLMs) into key stages of user interest mining, item retrieval, and explanation generation, RecGPT transforms log-fitting recommendation into an intent-centric process. To effectively align general-purpose LLMs to the above domain-specific recommendation tasks at scale, RecGPT incorporates a multi-stage training paradigm, which integrates reasoning-enhanced pre-alignment and self-training evolution, guided by a Human-LLM cooperative judge system. Currently, RecGPT has been fully deployed on the Taobao App. Online experiments demonstrate that RecGPT achieves consistent performance gains across stakeholders: users benefit from increased content diversity and satisfaction (e.g., CICD +5.53%, DT +4.35%), merchants and the platform gain greater exposure and conversions (e.g., CTR +5.65%, IPV +7.55%, DCAU +2.46%), and sustained long-term retention improvements (LT-30 +1.63%). These comprehensive improvement results across all stakeholders validate that our intent-centric design can foster a more sustainable and mutually beneficial ecosystem.
Jiakai Tang, Wen Chen, Dian Chen et al.· ACM Transactions on Informat...· 0 citations
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