Netflix PRS 2024 - Applying LLMs to Recommendation Experiences

[ llm engineering production leadership ] · 2 min read

Recently, I was invited to speak at the 2024 Netflix Workshop on Personalization, Recommendation, and Search. I shared about the challenges faces while building and deploying LLM-powered recommendation experiences at consumer scale.

It was an enlightening conference that covered a range of topics from LLMs to recsys to measurement, and it was a fun opportunity to catch up with old friends in San Francisco. I shared some observations here and here. Here’s the full list of topics and speakers.

  • LLMs as Agents and How to Evaluate Them on Real-World Tasks (Alane Suhr, Assistant Professor, UC Berkeley)
  • Applying Language Models to Recommendation Experiences: Challenges and Lessons (Eugene Yan, Senior Applied Scientist, Amazon)
  • Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations (Jiaqi Zhai, Distinguished Engineer, Meta)
  • Conversational RecSys (Harald Steck, Senior Research Scientist, Netflix)
  • Long-Term Value of Exploration: Measurements, Findings and Algorithms (Yi Su, Research Scientist, Google)
  • Beyond the Binge: Recommending for Long-Term Member Satisfaction (Jiangwei Pan, Senior Research Scientist, Netflix)
  • Toward Practical Robustness in AI (Alex Beutel, Model Safety Tech Lead, OpenAI)
  • Building Airbnb Categories with ML and Human in the loop (Mihajlo Grbovic, Principal ML Scientist, AirBnB)
  • Personalization at Spotify (Maria Dimakopoulou, Director of ML & Head of Homepage P13N, Spotify)

If you found this useful, please cite this write-up as:

Yan, Ziyou. (May 2024). Netflix PRS 2024 - Applying LLMs to Recommendation Experiences.


  title   = {Netflix PRS 2024 - Applying LLMs to Recommendation Experiences},
  author  = {Yan, Ziyou},
  journal = {},
  year    = {2024},
  month   = {May},
  url     = {}

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