---
title: "Building a unified consumer memory for personalization at scale"
url: https://daily.dev/posts/building-a-unified-consumer-memory-for-personalization-at-scale-emiywz2l0
source_url: https://careersatdoordash.com/blog/doordash-unified-consumer-memory-for-personalization-at-scale
type: article
source: "Doordash"
published: 2026-06-08T22:15:27.581Z
updated: 2026-06-08T22:15:59.972Z
tags: ["llm", "embeddings", "recommendation-systems"]
reading_time: 16
upvotes: 0
comments: 0
language: en
---

> ## Documentation Index
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# Building a unified consumer memory for personalization at scale

**[Doordash](https://daily.dev/sources/doordash)** · 16 min read · 0 upvotes · 0 comments

## Summary

DoorDash built a unified consumer memory platform that extracts semantic understanding from behavioral signals (orders, browsing, searches) and makes it available across all personalization surfaces. The system uses LLMs to generate structured 'memory blocks' — versioned, domain-specific summaries like dietary preferences, brand affinities, and dining patterns — organized into three layers: long-term, in-session, and explicit context. These memory blocks are encoded two ways for ML consumption: dense asymmetric embeddings for semantic similarity matching, and a heterogeneous context graph for relational reasoning between consumers, brands, taxonomies, and concepts. The platform powers personalized carousels and ranking models, with selective recomputation to manage LLM compute costs. Key lessons include treating memory blocks as semantic matching primitives for both ML and LLM systems, decoupling extraction from encoding, and maintaining full versioning/lineage for every component.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://careersatdoordash.com/blog/doordash-unified-consumer-memory-for-personalization-at-scale>

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---

Tags: [#llm](https://daily.dev/tags/llm), [#embeddings](https://daily.dev/tags/embeddings), [#recommendation-systems](https://daily.dev/tags/recommendation-systems)

[View this post on daily.dev](https://daily.dev/posts/building-a-unified-consumer-memory-for-personalization-at-scale-emiywz2l0)