Papers by Lukasz Heldt
Aligning Large Language Models with Recommendation Knowledge (2024.findings-naacl)
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Yuwei Cao, Nikhil Mehta, Xinyang Yi, Raghunandan Hulikal Keshavan, Lukasz Heldt, Lichan Hong, Ed Chi, Maheswaran Sathiamoorthy
| Challenge: | Large language models (LLMs) excel at natural language reasoning, but cannot model complex user-item interactions inherent in recommendation tasks. |
| Approach: | They propose to equip large language models with recommendation-specific knowledge to address this gap by combining Masked Item Modeling and Bayesian Personalized Ranking (BPR) auxiliary task data samples are generated that encode item correlations and user preferences. |
| Outcome: | Experiments on Amazon Toys & Games, Beauty, and Sports & Outdoors show that the proposed method outperforms conventional and LLM-based baselines by significant margins in retrieval. |