Recsperts - Recommender Systems Experts

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.

We 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.

We 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: replacing dozens of taxonomy-based dense features in the ranking model with Semantic ID n-gram aggregations while improving online metrics, and using Semantic IDs for query reformulation in search, letting the system traverse a learned hierarchy to refine or diversify a query. This connects to DoorDash's own paper on the topic and to a broader conversation about why search, recommendation and agentic ordering — DoorDash's own "Ask DoorDash" — are converging on a shared substrate of Semantic IDs and consumer memory, while today's app surfaces still need to grow more flexible for that convergence to feel seamless.

We close with a preview of the RecSys 2026 tutorial "Recommender Systems in Delivery Platforms: Challenges, Solutions and Learnings," which Raghav is co-presenting with Wolt's Paavo Camps and myself, and his advice for navigating a field that reinvents itself every quarter: be honest about whether that pace suits you, use AI agents to filter what's worth your attention, and build the judgment to recognize dead ends early.

Enjoy this enriching episode of RECSPERTS – Recommender Systems Experts.
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