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        <title>PepRec: Progressive Enhancement of Prompting for Recommendation</title>
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        <namePart type="given">Yakun</namePart>
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    <name type="personal">
        <namePart type="given">Shi-ang</namePart>
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    <abstract>With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, recent researchers have actively explored the potential of LLMs for recommendation systems by converting the input data into textual sentences through prompt templates. Although semantic knowledge from LLMs can help enrich the content information of items, to date it is still hard for them to achieve comparable performance to traditional deep learning recommendation models, partly due to a lack of ability to leverage collaborative filtering. In this paper, we propose a novel training-free prompting framework, PepRec, which aims to capture knowledge from both content-based filtering and collaborative filtering to boost recommendation performance with LLMs, while providing interpretation for the recommendation. Experiments based on two real-world datasets from different domains show that PepRec significantly outperforms various traditional deep learning recommendation models and prompt-based recommendation systems.</abstract>
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        <date>2024-11</date>
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%0 Conference Proceedings
%T PepRec: Progressive Enhancement of Prompting for Recommendation
%A Yu, Yakun
%A Qi, Shi-ang
%A Li, Baochun
%A Niu, Di
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F yu-etal-2024-peprec
%X With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, recent researchers have actively explored the potential of LLMs for recommendation systems by converting the input data into textual sentences through prompt templates. Although semantic knowledge from LLMs can help enrich the content information of items, to date it is still hard for them to achieve comparable performance to traditional deep learning recommendation models, partly due to a lack of ability to leverage collaborative filtering. In this paper, we propose a novel training-free prompting framework, PepRec, which aims to capture knowledge from both content-based filtering and collaborative filtering to boost recommendation performance with LLMs, while providing interpretation for the recommendation. Experiments based on two real-world datasets from different domains show that PepRec significantly outperforms various traditional deep learning recommendation models and prompt-based recommendation systems.
%U https://aclanthology.org/2024.emnlp-main.995
%P 17941-17953

Markdown (Informal)

[PepRec: Progressive Enhancement of Prompting for Recommendation](https://aclanthology.org/2024.emnlp-main.995) (Yu et al., EMNLP 2024)

• PepRec: Progressive Enhancement of Prompting for Recommendation (Yu et al., EMNLP 2024)

ACL

• Yakun Yu, Shi-ang Qi, Baochun Li, and Di Niu. 2024. PepRec: Progressive Enhancement of Prompting for Recommendation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17941–17953, Miami, Florida, USA. Association for Computational Linguistics.