Sheetal Borar
Sheetal Borar is a senior applied scientist at Etsy, where she works on retrieval systems powering large-scale recommender systems. She has spoken at PyData Global and PyData NYC and has several publications under her name and is recognized as a strong advocate for knowledge sharing and community building. She has gained experience across multiple industries and has about five years of professional experience in building machine learning solutions.
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.