The article explains the design, core concepts, algorithms, and implementation details of Linux 6.6's default EEVDF scheduler, compares it with the legacy CFS, provides practical commands for inspection and switching, and discusses real‑world scenarios and common pitfalls for developers and interview candidates.
The article walks through the evolution of CPU scheduling—from naïve first‑come‑first‑served queues to priority‑based, time‑slice, and finally the Completely Fair Scheduler—illustrating each approach with code, highlighting pitfalls like starvation, and showing how tracking virtual runtime yields fair and responsive task execution.
Economics studies how societies allocate scarce resources to satisfy unlimited wants, exploring concepts such as scarcity, choice, efficiency versus fairness, institutional incentives, rationality versus behavior, and the interplay between micro and macro perspectives, while highlighting its practical relevance in policy, business, and personal decisions.
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.
The article examines the rapid rise of AI interview software, highlighting its appeal to job seekers, the potential for cheating, expert criticism, associated risks, and the need for balanced, ethical approaches to ensure fairness in recruitment.
This article examines the challenges of high‑concurrency flash‑sale scenarios in multi‑active architectures, analyzes fairness issues caused by geographic latency, and presents practical solutions such as data‑sharding and global‑clock ordered queues, complemented by a Redis‑based implementation example.
The article explains how the Earliest Eligible Virtual Deadline First (EEVDF) scheduler, introduced in Linux 6.6, replaces CFS by using virtual deadlines and a lag concept to achieve both fairness and interactivity, and walks through concrete formulas, parameter settings, and step‑by‑step scheduling examples.
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.
The 30th SIGKDD conference in Barcelona featured 2,046 research papers with a 20% acceptance rate, and this article compiles the 59 recommendation‑system papers—covering large‑model recommenders, graph‑based methods, sequential models, fairness, privacy, advertising, debiasing, reinforcement learning and more—for researchers to explore the latest academic advances.
The article examines the drawbacks of result‑oriented performance appraisal, arguing that over‑emphasis on outcomes neglects process, fairness, and employee motivation, and proposes balanced evaluation, fair metrics, strategic clarity, organizational efficiency, and performance dashboards to improve management practices.
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, and proposes causal embedding models to mitigate unfairness while ensuring sustainable system performance.
This article reviews a series of recent research papers that explore how generative AI, especially large language models, can be integrated into recommender systems to improve next‑basket recommendation, instruction‑following, zero‑shot ranking, fairness evaluation, and generative retrieval paradigms.
PerBid introduces a personalized automated bidding framework that creates context‑aware RL agents for advertiser clusters using a profiling network to embed static and dynamic campaign features, and experiments on Alibaba’s display‑ad platform show up to 10.85% performance gains while markedly improving fairness across heterogeneous advertisers.
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.
This article presents a comprehensive overview of trustworthy intelligent decision-making, introducing a decision framework and discussing counterfactual reasoning, complex reward modeling, predictive fairness, and regulatory constraints, while highlighting practical methods and recent research advances in each sub‑area.
This article examines the technical foundations of lottery randomness, explains linear congruential generators and their parameter constraints, discusses implementation pitfalls such as low‑bit bias, and reviews several practical lottery algorithms—including selection, shuffle, reservoir sampling, red‑packet splitting, and probability methods—to achieve fair and scalable interactive draws.
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.
This article compares five flash‑sale (seckill) system designs, detailing their implementation principles, pros and cons, fairness, scalability, and provides guidance on selecting the most suitable architecture for different traffic and operational constraints.
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.
This article details how Alibaba’s entertainment team designed and implemented a robust, cloud‑native architecture for the 2019 Double 11 “Cat Night” live‑stream, addressing technical goals, fairness, high‑concurrency spikes, dynamic routing, peak‑shaving, downstream protection, and multi‑screen interaction to deliver a seamless consumer experience.
This article provides a comprehensive analysis of Java's SynchronousQueue, detailing its unique zero-capacity behavior, differences from other BlockingQueue implementations, internal structures such as TransferQueue and TransferStack, fair and non‑fair modes, and the core transfer algorithm with code examples.
