Hao Zeng

Yesterday I read this great article for the upcoming EMNLP about product review summaries from a few folks at Wayfair: End-to-End Aspect-Guided Review Summarization at Scale by Ilya Boytsov, Vinny DeGenova, Mikhail Balyasin, Joe Walt, Caitlin Eusden, Marie-Claire Rochat, Margaret Pierson Retailers: this is how review summaries should be done. Wayfair’s new paper shows an end‑to‑end, aspect‑guided approach that turns thousands of raw reviews into a short, trustworthy product summary shoppers can actually use. What’s novel for retail: - Evidence‑first UX: The model writes ~300–500‑character copy anchored to representative reviews. Shoppers can tap aspects like comfort or delivery to dive in. - Real‑time ops: Auto‑generate once a product hits 10 reviews and then auto‑refresh when new reviews grow enough. - Built for scale: Cached aspect mappings + sampling keep costs and latency in check without losing signal. - Proven impact: AB testing shows lifted ATC and CVR and cut bounce with no hit to revenue or page speed. - Open data: ~12m anonymized reviews across 92k products with extracted aspects + generated summaries available on Hugging Face. Open data is a key to comparing models across industries and use cases. Why it matters: A trustworthy, interpretable, continuously fresh review UX that boosts confidence and carts.

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Today we’re releasing the Pink Paper. Here’s the cleanest signal in the data. When you compare Hermès and LVMH across nearly 20 years, the divergence isn’t brand positioning or demand mix. It’s structural volatility. One metric captures it clearly: Operating Margin Volatility (YoY): • Hermès: ~7% • LVMH: ~14% In other words, LVMH’s operating margins fluctuate at roughly twice the amplitude. This matters because margin volatility reflects how a business absorbs: • Cost shocks • Demand cycles • Capital allocation decisions Lower volatility indicates tighter internal coordination between pricing, production, and growth pacing. Higher volatility reflects exposure introduced by acquisitions, portfolio breadth, and integration cycles. Across margins, cash conversion, long-horizon CAGR, productivity, and volatility, the data consistently points to the same conclusion: Hermès compounds coherence. LVMH compounds scale. The Pink Paper examines why only one of these models produces durable financial stability over cycles.

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Excited to share that our team’s paper on aspect-guided review summarization has been accepted to EMNLP 2025’s industry track! 📈🧠🤓 In this paper, we describe a live system at Wayfair that leverages LLMs alongside classical NLP techniques to summarize tens of millions of customer reviews, making it easier for shoppers to discover what matters most to them on site. To further support the research community, we’re also open-sourcing a dataset of ~12M anonymized reviews and their extracted aspects which we hope accelerates progress in aspect extraction, sentiment analysis, and summarization tasks. This milestone reflects the hard work of many folks across Wayfair, including our co-authors Ilya Boytsov, Misha Balyasin, Joe Walt, Caitlin Eusden, Marie-Claire Rochat, and Margaret Pierson. https://lnkd.in/ezJMi3g7 #NLP #EMNLP #EMNLP2025 #LLMs #Summarization #ABSA #OpenSource #Data #Dataset #ecommerce #wayfair #machinelearning #ai #publication

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Generative AI is everywhere in the hype cycle right now. But at Walmart, we’re seeing the biggest wins not from chasing the hype, but from hybrid approaches that bring multiple flavors of AI together. • For data science teams, “vibe coding” accelerates exploration and prototyping. • Machine learning still does the heavy lifting on modeling and process automation. • And then we use genBI (generative AI for business intelligence) as the user interface — turning complex model outputs into something business partners can actually use and drill into with follow-up questions. This genAI-as-UI strategy is powerful: it opens up ML insights to a much broader audience without piling extra requests on the data science team. Business partners get answers faster, and our team can stay focused on advancing the models. As this Quartz article (https://lnkd.in/eSPtKMCD) puts it, LLMs aren’t replacing traditional machine learning — they’re enhancing it. That rings true in our experience: the real value comes when each type of AI does what it does best, and we knit them together. The hype is fun, but right now the wins are hybrid.

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We're hiring three AI Analytics Engineers on the Data Science & Analytics team at Airtable. These are early to mid-career roles for builders who are excited about working at the intersection of data science, analytics, and AI. One of these roles will report directly to me as we spin up a new AI & Analytics Platform function. Things are moving fast. The most impactful work in data right now isn't building models — it's fundamentally changing how a company gets answers from its data. That's where my focus is, and it's why Airtable is investing in a new team to own it. Over the past six months, our team stopped experimenting with AI on the side and restructured around it: - Agent-powered development workflows — I built shared AI systems for the data team embedded in how we write SQL, build pipelines, document business logic, and debug. Team members are regularly seeing 2x to 10x speed improvements — not from shortcuts, but from AI woven into every step of how we build. - Conversational analytics — Ad-hoc questions that used to fill our queue from leadership, product, GTM, and finance are increasingly answered by an AI agent — in natural language, where people already work. -A context layer that makes it trustworthy — We built a system that gives the agent real business knowledge — which table is correct for a given analysis, how metrics differ across teams, why something spiked last quarter. Context that usually lives in an analyst's head. - A learning loop that compounds — Every user correction becomes a permanent improvement, not a one-off fix. — We're doubling down. Airtable is standing up an AI & Analytics Platform function within DS&A to keep building at the intersection of data science, ML, analytics, and AI — and I'm leading it. We need more builders. AI Analytics Engineer, AI & Analytics Platform First hire on this team. You'll build the context layer that makes AI tools accurate, design evaluation frameworks that make them trustworthy, and develop agent systems that let the org self-serve with confidence. You'll work directly with me to shape what this function becomes. https://lnkd.in/gFstQqQb We're also hiring two roles embedded in other parts of the business: AI Analytics Engineer, Marketing Analytics Own data infrastructure, dashboards, and AI-native tooling for Marketing. Build the canonical data sources leadership relies on. https://lnkd.in/gYHS55ma AI Analytics Engineer, Business Analytics Own financial data models powering ACV, ARR, billings, and revenue. Partner with a Finance team that's building their own AI agents and workflows. You'll build the data foundation they run on. https://lnkd.in/gV5hnJjG — I'm looking for people who build before they're asked. Who have a side project that started as "I wonder if I could..." and turned into something real. Who pick up a new tool and immediately try to break it. If that sounds like you — reach out or apply. I'd love to talk.

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Fun fact: Some of the hardest math at Amazon isn’t about AI, it’s about trucks. Curious how we solve it? We built a solver, and today, it’s open-source. 🚀 👉 Check it out & drop us a ⭐: https://lnkd.in/epTCYN8a Meet 𝗝𝘂𝗟𝗦 (Julia Local Search), Amazon Science’s new open-source solver: Developed by scientists, for scientists, JuLS is fast, extensible, and open to community contributions. It combines Constraint-Based Local Search and Constraint Programming, and it’s already deployed in Amazon’s production environment to tackle complex middle-mile logistics and scheduling. JuLS performs on par with Google OR-Tools on standard tasks. Its real strength lies in handling black-box objectives and constraints, making it a powerful solution for real-world optimization scenarios that often require interfacing with proprietary or external systems. More than a solver, JuLS is a playground for researchers and practitioners tackling combinatorial optimization problems. Our team : Axel Navarro, Arthur Dupuis, Ilan Coulon, Maxime Mulamba Ke Tchomba, El Mehdi OUDAOUD

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