Large language models make sample-efficient recommender systems

Jianghao LIN, Xinyi DAI, Rong SHAN, Bo CHEN, Ruiming TANG, Yong YU, Weinan ZHANG

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Front. Comput. Sci. ›› 2025, Vol. 19 ›› Issue (4) : 194328. DOI: 10.1007/s11704-024-40039-z
Artificial Intelligence
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Large language models make sample-efficient recommender systems

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Jianghao LIN, Xinyi DAI, Rong SHAN, Bo CHEN, Ruiming TANG, Yong YU, Weinan ZHANG. Large language models make sample-efficient recommender systems. Front. Comput. Sci., 2025, 19(4): 194328 https://doi.org/10.1007/s11704-024-40039-z

References

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[1]
Zhang J, Bao K, Zhang Y, Wang W, Feng F, He X. Large language models for recommendation: progresses and future directions. In: Proceedings of the ACM on Web Conference 2024. 2024, 1268−1271
[2]
Pan X, Wu L, Long F, Ma A . Exploiting user behavior learning for personalized trajectory recommendations. Frontiers of Computer Science, 2022, 16( 3): 163610
[3]
MindSpore, 2020

Acknowledgements

The Shanghai Jiao Tong University team was partially supported by the National Natural Science Foundation of China (Grant No. 62177033). Jianghao Lin is supported by the Wu Wen Jun Honorary Doctoral Scholarship. The work was sponsored by Huawei Innovation Research Program. We thank MindSpore [3] for the partial support of this work, which is a new deep learning computing framework.

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The authors declare that they have no competing interests or financial conflicts to disclose.

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