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Cascade Ranking is a prevalent architecture in large-scale top-k selection systems like recommendation and advertising platforms.
The perceptron: a probabilistic model for information storage and organization in the brain
Rosenblatt, F · 1958
Earlier work this paper cites.
Pranking with ranking
Crammer, K. and Singer, Y · 2001
Earlier work this paper cites.
Learning to rank using gradient descent
Burges, C. J. C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., and Hullender, G. N · 2005
Earlier work this paper cites.
Mcrank: Learning to rank using multiple classification and gradient boosting
Li, P., Burges, C. J. C., and Wu, Q · 2007
Earlier work this paper cites.
From ranknet to lambdarank to lambdamart: An overview
Burges, C. J · 2010
Earlier work this paper cites.
A cascade ranking model for efficient ranked retrieval
Wang, L., Lin, J., and Metzler, D · 2011
Earlier work this paper cites.
Learning deep structured semantic models for web search using clickthrough data
Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., and Heck, L · 2013
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
Earlier work this paper cites.
Deep neural networks for youtube recommendations
Covington, P., Adams, J., and Sargin, E · 2016
Earlier work this paper cites.
Ordinal regression with multiple output cnn for age estimation
Niu, Z., Zhou, M., Wang, L., Gao, X., and Hua, G · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Earlier work this paper cites.
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Kendall, A., Gal, Y., and Cipolla, R · 2018
Earlier work this paper cites.
Entire space multi-task model: An effective approach for estimating post-click conversion rate
Ma, X., Zhao, L., Huang, G., Wang, Z., Hu, Z., Zhu, X., and Gai, K · 2018
Earlier work this paper cites.
The lambdaloss framework for ranking metric optimization
Wang, X., Li, C., Golbandi, N., Bendersky, M., and Najork, M · 2018
Earlier work this paper cites.
Deep interest network for click-through rate prediction
Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., Yan, Y., Jin, J., Li, H., and Gai, K · 2018
Earlier work this paper cites.
Differentiable ranking and sorting using optimal transport
Cuturi, M., Teboul, O., and Vert, J.-P · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Joint optimization of cascade ranking models
Gallagher, L., Chen, R., Blanco, R., and Culpepper, J. S · 2019
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Stochastic optimization of sorting networks via continuous relaxations
Grover, A., Wang, E., Zweig, A., and Ermon, S · 2019
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Task agnostic meta-learning for few-shot learning
Jamal, M. A. and Qi, G.-J · 2019
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End-to-end multi-task learning with attention
Liu, S., Johns, E., and Davison, A. J · 2019
Cited alongside, same era.
Rankflow: Joint optimization of multi-stage cascade ranking systems as flows
Qin, J., Zhu, J., Chen, B., Liu, Z., Liu, W., Tang, R., Zhang, R., Yu, Y., and Zhang, W · 2022
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Croloss: towards a customizable loss for retrieval models in recommender systems
Tang, Y., Bai, W., Li, G., Liu, X., and Zhang, Y · 2022
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Listwise learning to rank based on approximate rank indicators
Thonet, T., Cinar, Y. G., Gaussier, E., Li, M., and Renders, J.-M · 2022
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Stochastic retrieval-conditioned reranking
Zamani, H., Bendersky, M., Metzler, D., Zhuang, H., and Wang, X · 2022
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Towards understanding the overfitting phenomenon of deep click-through rate models
Zhang, Z.-Y., Sheng, X.-R., Zhang, Y., Jiang, B., Han, S., Deng, H., and Zheng, B · 2022
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FAA: fine-grained attention alignment for cascade document ranking
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Fast differentiable sorting and ranking
Blondel, M., Teboul, O., Berthet, Q., and Djolonga, J · 2020
Cited alongside, same era.
Just pick a sign: Optimizing deep multitask models with gradient sign dropout
Chen, Z., Ngiam, J., Huang, Y., Luong, T., Kretzschmar, H., Chai, Y., and Anguelov, D · 2020
Cited alongside, same era.
Softsort: A continuous relaxation for the argsort operator
Prillo, S. and Eisenschlos, J · 2020
Cited alongside, same era.
Differentiable sorting networks for scalable sorting and ranking supervision
Petersen, F., Borgelt, C., Kuehne, H., and Deussen, O · 2021
Cited alongside, same era.
Neuralndcg: Direct optimisation of a ranking metric via differentiable relaxation of sorting
Pobrotyn, P. and Białobrzeski, R · 2021
Cited alongside, same era.
Pirank: Scalable learning to rank via differentiable sorting
Swezey, R. M. E., Grover, A., Charron, B., and Ermon, S · 2021
Cited alongside, same era.
Li, Z., Tao, C., Feng, J., Shen, T., Zhao, D., Geng, X., and Jiang, D · 2023
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Tree based progressive regression model for watch-time prediction in short-video recommendation
Lin, X., Chen, X., Song, L., Liu, J., Li, B., and Jiang, P · 2023
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Fast, differentiable and sparse top-k: a convex analysis perspective
Sander, M. E., Puigcerver, J., Djolonga, J., Peyré, G., and Blondel, M · 2023
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Joint optimization of ranking and calibration with contextualized hybrid model
Sheng, X.-R., Gao, J., Cheng, Y., Yang, S., Han, S., Deng, H., Jiang, Y., Xu, J., and Zheng, B · 2023
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Xb-maml: Learning expandable basis parameters for effective meta-learning with wide task coverage
Lee, J.-J. and Yoon, S. W · 2024
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Adaptive neural ranking framework: Toward maximized business goal for cascade ranking systems
Wang, Y., Wang, Z., Yang, J., Wen, S., Kong, D., Li, H., and Gai, K · 2024
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On the effectiveness of sampled softmax loss for item recommendation
Wu, J., Wang, X., Gao, X., Chen, J., Fu, H., and Qiu, T · 2024
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Full stage learning to rank: A unified framework for multi-stage systems
Zheng, K., Zhao, H., Huang, R., Zhang, B., Mou, N., Niu, Y., Song, Y., Wang, H., and Gai, K · 2024
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Recflow: An industrial full flow recommendation dataset
Liu, Q., Zheng, K., Huang, R., Li, W., Cai, K., Chai, Y., Niu, Y., Hui, Y., Han, B., Mou, N., Wang, H., Bao, W., Yu, Y., Zhou, G., Li, H., Song, Y., Lian, D., and Gai, K · 2025
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