Astha Puri
Astha is a Senior Data Scientist at CVS Health, where she leads the design of recommendation engines for digital platforms, helping customers discover the right products and enabling patients to access the appropriate health services and support. She specializes in home screen personalization, leveraging data-driven insights to enhance user experiences. With a strong background in the tech industry, she is now applying her expertise to transform and innovate within the healthcare sector.
Session
Recommender systems power everything from e-commerce to media streaming, but most pipelines still rely on collaborative filtering or neural models that focus narrowly on user–item interactions. Large language models (LLMs), by contrast, excel at reasoning across unstructured text, contextual information, and explanations.
This tutorial bridges the two worlds. Participants will build a hybrid recommender system that uses structured embeddings for retrieval and integrates an LLM layer for personalization and natural-language explanations. We’ll also discuss practical engineering constraints: scaling, latency, caching, distillation/quantization, and fairness.
By the end, attendees will leave with a working hybrid recommender they can extend for their own data, along with a playbook for when and how to bring LLMs into recommender workflows responsibly.