Improve Temporal Awareness of LLMs for Sequential Recommendation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911866892058624 |
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| author | Chu, Zhendong Wang, Zichao Zhang, Ruiyi Ji, Yangfeng Wang, Hongning Sun, Tong |
| author_facet | Chu, Zhendong Wang, Zichao Zhang, Ruiyi Ji, Yangfeng Wang, Hongning Sun, Tong |
| contents | Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks. However, it is empirically found that LLMs fall short in recognizing and utilizing temporal information, rendering poor performance in tasks that require an understanding of sequential data, such as sequential recommendation. In this paper, we aim to improve temporal awareness of LLMs by designing a principled prompting framework inspired by human cognitive processes. Specifically, we propose three prompting strategies to exploit temporal information within historical interactions for LLM-based sequential recommendation. Besides, we emulate divergent thinking by aggregating LLM ranking results derived from these strategies. Evaluations on MovieLens-1M and Amazon Review datasets indicate that our proposed method significantly enhances the zero-shot capabilities of LLMs in sequential recommendation tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02778 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improve Temporal Awareness of LLMs for Sequential Recommendation Chu, Zhendong Wang, Zichao Zhang, Ruiyi Ji, Yangfeng Wang, Hongning Sun, Tong Information Retrieval Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks. However, it is empirically found that LLMs fall short in recognizing and utilizing temporal information, rendering poor performance in tasks that require an understanding of sequential data, such as sequential recommendation. In this paper, we aim to improve temporal awareness of LLMs by designing a principled prompting framework inspired by human cognitive processes. Specifically, we propose three prompting strategies to exploit temporal information within historical interactions for LLM-based sequential recommendation. Besides, we emulate divergent thinking by aggregating LLM ranking results derived from these strategies. Evaluations on MovieLens-1M and Amazon Review datasets indicate that our proposed method significantly enhances the zero-shot capabilities of LLMs in sequential recommendation tasks. |
| title | Improve Temporal Awareness of LLMs for Sequential Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2405.02778 |