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Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them.
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W. Guo, C. Zhu, F. Yan, B. Chen, W. Liu, H. Guo, H. Zheng, Y. Liu, and R. Tang, “DFFM: domain facilitated feature modeling for CTR prediction,” in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023 , I. Frommholz, F. Hopfgartner, M. Lee, M. Oakes, M. Lalmas, M. Zhang, and R. L. T. Santos, Eds. Birmingham, United Kingdom: ACM, 2023, pp. 4602–4608. [Online]. Available: https://doi.org/10.1145/3583780.3615469
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M. Xia, T. Gao, Z. Zeng, and D. Chen, “Sheared llama: Accelerating language model pre-training via structured pruning,” 2023
2023
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2023
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Y. Luo, S. Ma, M. Nie, C. Peng, Z. Lin, J. Shao, and Q. Xu, “Domain-aware cross-attention for cross-domain recommendation,” 2024
2024
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