Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations
In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity for personalization innovation. A key challenge lies in solving the "cold start" problem for users. This paper introduces a novel framework for enhancing recommendation quality by transferring knowledge from data-rich verticals (e.g., restaurants at DoorDash) to data-sparse ones. We leverage Large Language Models (LLMs) to perform generative inference, synthesizing sparse, high-dimensional features that encapsulate latent user affinities. Specifically, we employ a hierarchical Retrieval-Augmented Generation (RAG) pipeline to derive multi-lev...
| Authors | Nimesh Sinha, Raghav Saboo, Martin Wang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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