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The award‑winning WSDM 2025 paper reveals how recommendation models magnify popularity bias through spectral alignment of the rating matrix’s leading singular vector with item popularity, and proposes the ReSN regularization technique to mitigate this bias efficiently.
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Kuaishou and Tsinghua University were honored with the first‑place Qian Weichang Chinese Information Processing Science and Technology Award for their collaborative content recommendation project, which achieved international‑level innovations in explainable recommendation, bias correction, and edge intelligence, and has been applied widely in Kuaishou's platform and top academic conferences.
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This article presents five peer‑reviewed papers accepted to the Kuaishou Community Science track at the 2023 ACM SIGIR conference, covering offline reinforcement learning for recommendation fairness, global residual value for fair item exposure, personalized QoE in short‑form video, multi‑behavior self‑supervised recommendation, and a search‑enhanced sequential recommendation framework.
This article examines the fairness of recommendation systems from both consumer and provider viewpoints, discussing sources of bias, definitions of equality and equity, measurement metrics such as CGF and MMF, causal embedding techniques, experimental results on MovieLens and Yelp, and future research directions.
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The article outlines Ant Group's comprehensive approach to promoting trustworthy AI in large‑scale industrial settings, detailing the four core pillars of robustness, explainability, privacy protection, and fairness, and describing practical methodologies, open platforms, and ecosystem collaborations that drive responsible AI deployment.
This article introduces Java's Semaphore as a thread‑synchronization tool, explains its flow‑control use cases such as database connections and parking lots, provides a complete parking‑lot simulation code sample, and analyzes the underlying AQS‑based implementation including fairness, acquire/release mechanics, and auxiliary methods.
The article details the technical goals, fairness mechanisms, high‑concurrency handling, dynamic routing, peak‑shaving, downstream protection, and on‑site screen interaction strategies used to deliver a seamless multi‑platform live‑stream experience for the Double‑11 Cat Night, while summarizing key takeaways for future large‑scale events.
Alibaba’s 2019 Double‑11 “Cat Evening” live show combined a unified codebase across Youku, Taobao and Tmall with synchronized clocks, latency‑measurement devices and SEI‑injected messages to guarantee fair, zero‑loss interactions, while employing dynamic routing, pre‑warming, peak‑shaving, downstream protection and rehearsed contingency plans to handle massive concurrency and ensure stable, high‑quality user experience.