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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” CoRR , vol. abs/1707.06347, 2017
Original
2017
Earlier work this paper cites.
C. Qin, H. Zhu, T. Xu, C. Zhu, L. Jiang, E. Chen, and H. Xiong, “Enhancing person-job fit for talent recruitment: An ability-aware neural network approach,” in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08-12, 2018 . ACM, 2018, pp. 25–34
2018
Earlier work this paper cites.
C. Zhu, H. Zhu, H. Xiong, C. Ma, F. Xie, P. Ding, and P. Li, “Person-job fit: Adapting the right talent for the right job with joint representation learning,” ACM Trans. Manag. Inf. Syst. , vol. 9, no. 3, pp. 12:1–12:17, 2018
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
R. Le, W. Hu, Y. Song, T. Zhang, D. Zhao, and R. Yan, “Towards effective and interpretable person-job fitting,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019 . ACM, 2019, pp. 1883–1892
2019
Earlier work this paper cites.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in NAACL-HLT (1) . Association for Computational Linguistics, 2019, pp. 4171–4186
2019
Earlier work this paper cites.
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized BERT pretraining approach,” CoRR , vol. abs/1907.11692, 2019
Original
2019
Earlier work this paper cites.
Z. Yang, Z. Dai, Y. Yang, J. G. Carbonell, R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” in NeurIPS , 2019, pp. 5754–5764
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.

Similar

F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, and P. Jiang, “Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer,” in Proceedings of the 28th ACM international conference on information and knowledge management , 2019, pp. 1441–1450
2019
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
B. Fu, H. Liu, Y. Zhu, Y. Song, T. Zhang, and Z. Wu, “Beyond matching: Modeling two-sided multi-behavioral sequences for dynamic person-job fit,” in Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11-14, 2021, Proceedings, Part II , ser. Lecture Notes in Computer Science, vol. 12682. Springer, 2021, pp. 359–375
2021
Cited alongside, same era.
B. Min, H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heinz, and D. Roth, “Recent advances in natural language processing via large pre-trained language models: A survey,” arXiv preprint arXiv:2111.01243 , 2021

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2023

Generative Job Recommendations with Large Language Model

Zheng, Zhi, Qiu, Zhaopeng, Hu, Xiao et al.

Understand

The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process.

Predominantly, current job recommendations deploy either collaborative filtering or person-job matching strategies.
However, these models tend to operate as "black-box" systems and lack the capacity to offer explainable guidance to job seekers.
Moreover, conventional matching-based recommendation methods are limited to retrieving and ranking existing jobs in the database, restricting their potential as comprehensive career AI advisors.

Built on

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” CoRR , vol. abs/1707.06347, 2017
Original
2017
Earlier work this paper cites.
C. Qin, H. Zhu, T. Xu, C. Zhu, L. Jiang, E. Chen, and H. Xiong, “Enhancing person-job fit for talent recruitment: An ability-aware neural network approach,” in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08-12, 2018 . ACM, 2018, pp. 25–34
2018
Earlier work this paper cites.
C. Zhu, H. Zhu, H. Xiong, C. Ma, F. Xie, P. Ding, and P. Li, “Person-job fit: Adapting the right talent for the right job with joint representation learning,” ACM Trans. Manag. Inf. Syst. , vol. 9, no. 3, pp. 12:1–12:17, 2018
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
R. Le, W. Hu, Y. Song, T. Zhang, D. Zhao, and R. Yan, “Towards effective and interpretable person-job fitting,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019 . ACM, 2019, pp. 1883–1892
2019
Earlier work this paper cites.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in NAACL-HLT (1) . Association for Computational Linguistics, 2019, pp. 4171–4186
2019
Earlier work this paper cites.
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized BERT pretraining approach,” CoRR , vol. abs/1907.11692, 2019
Original
2019
Earlier work this paper cites.
Z. Yang, Z. Dai, Y. Yang, J. G. Carbonell, R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” in NeurIPS , 2019, pp. 5754–5764
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.

Similar

F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, and P. Jiang, “Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer,” in Proceedings of the 28th ACM international conference on information and knowledge management , 2019, pp. 1441–1450
2019
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
B. Fu, H. Liu, Y. Zhu, Y. Song, T. Zhang, and Z. Wu, “Beyond matching: Modeling two-sided multi-behavioral sequences for dynamic person-job fit,” in Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11-14, 2021, Proceedings, Part II , ser. Lecture Notes in Computer Science, vol. 12682. Springer, 2021, pp. 359–375
2021
Cited alongside, same era.
B. Min, H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heinz, and D. Roth, “Recent advances in natural language processing via large pre-trained language models: A survey,” arXiv preprint arXiv:2111.01243 , 2021

Then

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2021

Cited alongside, same era.

Z. Qiu, X. Wu, J. Gao, and W. Fan, “U-bert: Pre-training user representations for improved recommendation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 5, 2021, pp. 4320–4327

2021

Cited alongside, same era.

L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in Neural Information Processing Systems , vol. 35, pp. 27 730–27 744, 2022

2022

Cited alongside, same era.

C. Yang, Y. Hou, Y. Song, T. Zhang, J. Wen, and W. X. Zhao, “Modeling two-way selection preference for person-job fit,” in RecSys ’22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18 - 23, 2022 . ACM, 2022, pp. 102–112

2022

Cited alongside, same era.

L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. F. Christiano, J. Leike, and R. Lowe, “Training language models to follow instructions with human feedback,” in NeurIPS , 2022

2022

Cited alongside, same era.

D. Sileo, W. Vossen, and R. Raymaekers, “Zero-shot recommendation as language modeling,” in Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II . Springer, 2022, pp. 223–230

2022

Cited alongside, same era.

N. Muennighoff, T. Wang, L. Sutawika, A. Roberts, S. Biderman, T. L. Scao, M. S. Bari, S. Shen, Z.-X. Yong, H. Schoelkopf, X. Tang, D. Radev, A. F. Aji, K. Almubarak, S. Albanie, Z. Alyafeai, A. Webson, E. Raff, and C. Raffel, “Crosslingual generalization through multitask finetuning,” 2022

2022

Cited alongside, same era.

K. Bao, J. Zhang, Y. Zhang, W. Wang, F. Feng, and X. He, “Tallrec: An effective and efficient tuning framework to align large language model with recommendation,” arXiv preprint arXiv:2305.00447 , 2023

Original

2023

Closest in time.

J. Zhang, R. Xie, Y. Hou, W. X. Zhao, L. Lin, and J.-R. Wen, “Recommendation as instruction following: A large language model empowered recommendation approach,” arXiv preprint arXiv:2305.07001 , 2023

Original

2023

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Y. Ji, Y. Deng, Y. Gong, Y. Peng, Q. Niu, B. Ma, and X. Li, “Belle: Be everyone’s large language model engine,” https://github.com/LianjiaTech/BELLE, 2023

2023

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H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar et al. , “Llama: Open and efficient foundation language models,” arXiv preprint arXiv:2302.13971 , 2023

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2023

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Z. Chen, J. Chen, H. Zhang, F. Jiang, G. Chen, F. Yu, T. Wang, J. Liang, C. Zhang, Z. Zhang, J. Li, X. Wan, H. Li, and B. Wang, “Llm zoo: democratizing chatgpt,” https://github.com/FreedomIntelligence/LLMZoo, 2023

2023

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Z. Chen, F. Jiang, J. Chen, T. Wang, F. Yu, G. Chen, H. Zhang, J. Liang, C. Zhang, Z. Zhang, J. Li, X. Wan, B. Wang, and H. Li, “Phoenix: Democratizing chatgpt across languages,” arXiv preprint arXiv:2304.10453 , 2023

Original

2023

Closest in time.