Personalized World Knowledge Lets Large Models Truly Understand Users in Generative Recommendation

This article introduces LWGR, a framework that uses personalized soft prompts generated by parallel codebooks and Lagrangian‑constrained knowledge fusion to integrate large language model world knowledge into generative recommendation, overcoming fixed‑prompt limitations and knowledge‑behavior conflicts, and demonstrates superior performance on public and industrial datasets with notable revenue gains in online A/B tests.

Lagrangian constraintLarge Language Modelgenerative recommendation

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Personalized World Knowledge Lets Large Models Truly Understand Users in Generative Recommendation