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RecSys 2022 Keynote - Is the Juice Worth the Squeeze?

Eugene Yan 23 September 2022, 00:00

An Amazon Books engineer presented a keynote at RecSys 2022 comparing online and batch recommender systems, examining when each approach is most effective. The talk covered three production systems at Amazon Books that demonstrate strengths of online recommenders in handling cold-start users, contextual personalization, and reducing data staleness. The presentation provided practical lessons from deploying online recommender systems at scale.

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Invited keynote at the Workshop for Online Recommender Systems and User Modeling (ORSUM)

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TLDRocket 21 July 2026

The open-weight AI model market hit an inflection point today as Alibaba and Kimi released trillion-parameter competitors that challenge the closed-model orthodoxy—just as Washington moved to restrict them. Alibaba's Qwen 3.8 Max (2.4 trillion parameters) and Kimi K3 (2.8T) are going open-weight, with K3 ranking first on Frontend Web App Arena and fourth on agentic benchmarks, signaling that scale and transparency can coexist. The timing matters: while U.S. policymakers weigh layered restrictions on Chinese open models, technical experts are pushing back hard, arguing that closed gardens actually hurt security and competition more than they help. The real fragmentation risk isn't technical—it's regulatory.

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RecSys 2022 Keynote - Is the Juice Worth the Squeeze?

Why it matters

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Every AI story that matters, in your inbox by 8am.

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