{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Recsperts - Recommender Systems Experts","title":"#34: From Consumer Memory to Semantic IDs: Generative RecSys for Quick Commerce with Raghav Saboo","html":"","width":"100%","height":180,"duration":6210,"description":"In episode 34 of Recsperts, I'm joined by Raghav Saboo, Staff Machine Learning Engineer at DoorDash and Tech Lead for Personalization and Search for New Verticals — groceries, convenience, retail, alcohol, pet supplies and more, beyond the original restaurant vertical. We discuss the particular challenges of personalized recommendation, ranking and search in quick commerce, recent trends in generative recommendations and the application of Semantic IDs to item ranking and query reformulation. Raghav's path into recommender systems started in chemical engineering before he moved into ML consulting, a Master's in Statistics, Machine Learning and Econometrics from Duke University, and building LLMs for new language launches on Amazon's Alexa AI, ahead of joining DoorDash.\nWe start with the marketplace itself: DoorDash connects consumers, merchants and couriers, and growing it means balancing the interests of all three so that the platform stays healthy for everyone on it. Raghav walks me through how his team frames the consumer side around three pillars — familiarity (surfacing what a consumer already trusts), affordability (matching price sensitivity and timely deals) and novelty (introducing new items and categories without adding friction). From there we get into how DoorDash uses LLMs to build \"memory blocks,\" structured natural-language representations of a consumer organized around semantic domains like dietary preference, pet ownership or trusted brands, and how these feed LLM-generated collections that get resolved into real items through embedding-based retrieval.\nWe then turn to DoorDash's move to generative approaches, centered on Semantic IDs: hierarchical product identifiers learned through recursive clustering of item content embeddings, forming a taxonomy that captures attributes a human-built catalog structure might miss — as Raghav puts it, \"within e-commerce, items really carry a lot of meaning.\" He walks me through two production use cases:...","thumbnail_url":"https://img.transistorcdn.com/5aEuXaIPxTFiKrepOVeTtB4JSWf3YlVUbAQdiV2LNzA/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzI0Mzc3LzE2MzIz/NDExMjMtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}