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- W4385473460 abstract "We investigate various prompting strategies for enhancing personalized recommendation performance with large language models (LLMs) through input augmentation. Our proposed approach, termed LLM-Rec, encompasses four distinct prompting strategies: (1) basic prompting, (2) recommendation-driven prompting, (3) engagement-guided prompting, and (4) recommendation-driven + engagement-guided prompting. Our empirical experiments show that incorporating the augmented input text generated by LLM leads to improved recommendation performance. Recommendation-driven and engagement-guided prompting strategies are found to elicit LLM's understanding of global and local item characteristics. This finding highlights the importance of leveraging diverse prompts and input augmentation techniques to enhance the recommendation capabilities with LLMs." @default.
- W4385473460 created "2023-08-02" @default.
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- W4385473460 date "2023-07-24" @default.
- W4385473460 modified "2023-09-23" @default.
- W4385473460 title "LLM-Rec: Personalized Recommendation via Prompting Large Language Models" @default.
- W4385473460 doi "https://doi.org/10.48550/arxiv.2307.15780" @default.
- W4385473460 hasPublicationYear "2023" @default.
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