
GOT4Rec leverages the "Graph of Thoughts" (GoT) prompting strategy within Large Language Models (LLMs) to significantly improve sequential recommendation accuracy by effectively capturing and integrating short-term, long-term, and collaborative user preferences.


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GOT4Rec: Using "Graph of Thoughts" and LLMs for Improved Sequential Recommendations


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The paper introduces DARec, a novel sequential recommendation model that leverages the power of large language models (LLMs) while addressing their limitations in capturing intra-item relations and long-term collaborative knowledge.


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Dynamic Adaptation of Large Language Models for Enhanced Sequential Recommendation by Incorporating Intra-Item and Collaborative Knowledge
