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Recommending suitable jobs to users is a critical task in online recruitment platforms, as it can enhance users' satisfaction and the platforms' profitability.
1908
Earlier work this paper cites.
X. Su and T. M. Khoshgoftaar, “A survey of collaborative filtering techniques,” Advances in artificial intelligence , vol. 2009, 2009
2009
Earlier work this paper cites.
Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer , vol. 42, no. 8, pp. 30–37, 2009
2009
Earlier work this paper cites.
S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, “Bpr: Bayesian personalized ranking from implicit feedback,” in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence , 2009, pp. 452–461
2009
Earlier work this paper cites.
X. He and T.-S. Chua, “Neural factorization machines for sparse predictive analytics,” in Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval , 2017, pp. 355–364
2017
Earlier work this paper cites.
H.-J. Xue, X.-Y. Dai, J. Zhang, S. Huang, and J. Chen, “Deep matrix factorization models for recommender systems,” in Proceedings of the 26th International Joint Conference on Artificial Intelligence , 2017, pp. 3203–3209
2017
Earlier work this paper cites.
X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative filtering,” in Proceedings of the 26th international conference on world wide web , 2017, pp. 173–182
2017
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 Transactions on Management Information Systems (TMIS) , vol. 9, no. 3, pp. 1–17, 2018
2018
Earlier work this paper cites.
D. Shen, H. Zhu, C. Zhu, T. Xu, C. Ma, and H. Xiong, “A joint learning approach to intelligent job interview assessment,” in 27th International Joint Conference on Artificial Intelligence, IJCAI 2018 . International Joint Conferences on Artificial Intelligence, 2018, pp. 3542–3548
2018
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 , 2018, pp. 25–34
2018
Earlier work this paper cites.
S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system: A survey and new perspectives,” ACM Computing Surveys (CSUR) , vol. 52, no. 1, pp. 1–38, 2019
2019
Earlier work this paper cites.
X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua, “Neural graph collaborative filtering,” in Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval , 2019, pp. 165–174
2019
Earlier work this paper cites.
X. Wang, X. He, Y. Cao, M. Liu, and T.-S. Chua, “Kgat: Knowledge graph attention network for recommendation,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , 2019, pp. 950–958
2019
Earlier work this paper cites.
R. Yan, R. Le, Y. Song, T. Zhang, X. Zhang, and D. Zhao, “Interview choice reveals your preference on the market: To improve job-resume matching through profiling memories,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2019, pp. 914–922
2019
Cited alongside, same era.
Y. Luo, H. Zhang, Y. Wen, and X. Zhang, “Resumegan: An optimized deep representation learning framework for talent-job fit via adversarial learning,” in Proceedings of the 28th ACM international conference on information and knowledge management , 2019, pp. 1101–1110
2019
Cited alongside, same era.
S. Bian, W. X. Zhao, Y. Song, T. Zhang, and J.-R. Wen, “Domain adaptation for person-job fit with transferable deep global match network,” in Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) , 2019, pp. 4810–4820
2019
Cited alongside, same era.
D. Sileo, W. Vossen, and R. Raymaekers, “Zero-shot recommendation as language modeling,” in European Conference on Information Retrieval . Springer, 2022, pp. 223–230
2022
Later among the works it cites.
C. Yang, Y. Hou, Y. Song, T. Zhang, J.-R. Wen, and W. X. Zhao, “Modeling two-way selection preference for person-job fit,” in Proceedings of the 16th ACM Conference on Recommender Systems , 2022, pp. 102–112
2022
Later among the works it cites.
W. X. Zhao, Z. Lin, Z. Feng, P. Wang, and J.-R. Wen, “A revisiting study of appropriate offline evaluation for top-n recommendation algorithms,” ACM Transactions on Information Systems , vol. 41, no. 2, pp. 1–41, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
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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 , 2019, pp. 1883–1892
2019
Cited alongside, same era.
J. Neve and I. Palomares, “Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systems,” in Proceedings of the 13th ACM conference on recommender systems , 2019, pp. 219–227
2019
Cited alongside, same era.
X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang, “Lightgcn: Simplifying and powering graph convolution network for recommendation,” in Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval , 2020, pp. 639–648
2020
Cited alongside, same era.
S. Bian, X. Chen, W. X. Zhao, K. Zhou, Y. Hou, Y. Song, T. Zhang, and J.-R. Wen, “Learning to match jobs with resumes from sparse interaction data using multi-view co-teaching network,” in Proceedings of the 29th ACM International Conference on Information & Knowledge Management , 2020, pp. 65–74
2020
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.
A. Da’u and N. Salim, “Recommendation system based on deep learning methods: a systematic review and new directions,” Artificial Intelligence Review , vol. 53, no. 4, pp. 2709–2748, 2020
2020
Cited alongside, same era.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, pp. 139–144, 2020
2020
Cited alongside, same era.
J. Jiang, S. Ye, W. Wang, J. Xu, and X. Luo, “Learning effective representations for person-job fit by feature fusion,” in Proceedings of the 29th ACM International Conference on Information & Knowledge Management , 2020, pp. 2549–2556
2020
Cited alongside, same era.
Y. Hou, X. Pan, W. X. Zhao, S. Bian, Y. Song, T. Zhang, and J.-R. Wen, “Leveraging search history for improving person-job fit,” in Database Systems for Advanced Applications: 27th International Conference, DASFAA 2022, Virtual Event, April 11–14, 2022, Proceedings, Part I . Springer, 2022, pp. 38–54
2022
Cited alongside, same era.
Z. Du, Y. Qian, X. Liu, M. Ding, J. Qiu, Z. Yang, and J. Tang, “Glm: General language model pretraining with autoregressive blank infilling,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2022, pp. 320–335
2022
Later among the works it cites.
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