arXiv:2606.06779v1 Announce Type: cross Abstract: 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 feature
The proliferation of multi-vertical platforms and the maturity of large language models are converging, making cross-domain knowledge transfer a critical challenge and opportunity in personalized recommendations.
This development allows for improved user experience and monetization in data-sparse verticals, offering a significant competitive advantage to platforms capable of effectively implementing such strategies.
Recommendation systems can now more effectively address cold-start problems and data scarcity by leveraging generative LLM inference for knowledge transfer across diverse product categories.
- · Multi-vertical e-commerce platforms
- · LLM developers
- · Consumers (improved personalization)
- · Small businesses in emerging verticals
- · Traditional recommendation systems
- · Platforms with siloed data strategies
Recommendation quality and user engagement on multi-vertical platforms will significantly improve, especially for newer product categories.
This enhanced personalization could lead to increased market share for platforms that successfully implement these LLM-driven techniques, potentially disrupting existing market dynamics.
The success of this approach may spur further research into generative AI for cross-domain knowledge transfer beyond recommendations, impacting areas like content creation and targeted advertising.
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Read at arXiv cs.AI