Leveraging LLM Reasoning Enhances Personalized Recommender Systems (2024.findings-acl)
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Alicia Tsai, Adam Kraft, Long Jin, Chenwei Cai, Anahita Hosseini, Taibai Xu, Zemin Zhang, Lichan Hong, Ed H. Chi, Xinyang Yi
| Challenge: | Recent advances have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting. |
| Approach: | They propose to use Large Language Models to perform tasks with subjectivity and personalized preferences as inputs to RecSys. |
| Outcome: | The proposed framework aligns with real human judgment on the coherence and faithfulness of LLM reasoning responses. |
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