Applying LLMs to Recommender Systems
Applying LLMs to Recommender Systems
Join us for an engaging session on "Applying LLMs to Recommender Systems". This session will delve into the intersection of large language models and recommender systems, making it perfect for those looking to expand their understanding of AI applications.
About the Speaker:
Our speaker for today, Ashmi Banerjee, is currently a doctoral researcher at the Chair of Connected Mobility at the Technical University of Munich. She has a master's degree in Computer Science and three years of industry experience across Germany. Ashmi is a renowned ML GDE, Google Developer Expert Community Award Winner 2023, DevelopHER 2022 Award Winner, and was named one of the WWCode Top 100 Technologists to Watch.
Session Highlights:
Introduction: Overview of Recommender Systems and LLMs.
LLM4Rec Approaches: Various methods for integrating LLMs into recommendation engines.
Evaluation Protocols: Best practices for assessing LLM-enhanced recommender systems.
Case Studies: Examples like ChatGPT as a Recommender and the P5 Paradigm.
Building Trustworthy LLM4Rec: Ensuring reliability and user trust in LLM-based recommender systems.
Connect with Ashmi Banerjee:
LinkedIn: ashmi-banerjee
Connect with Us:
Website: https://aiplanet.com/
LinkedIn: AIPlanet
Instagram: @aiplanethub
Twitter: @aiplanethub
Stay tuned for an informative session that will advance your knowledge of AI. Don't miss this opportunity to learn from an experts in the field!