Netflix PRS 2024 – Applying LLMs to Recommendation Experiences

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. eugeneyan.com.
https://eugeneyan.com/speaking/netflix-prs/.

or

@article{yan2024prs,
  title   = {Netflix PRS 2024 - Applying LLMs to Recommendation Experiences},
  author  = {Yan, Ziyou},
  journal = {eugeneyan.com},
  year    = {2024},
  month   = {May},
  url     = {https://eugeneyan.com/speaking/netflix-prs/}
}

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