The LambdaLoss Framework for Ranking Metric Optimization
Open accessAbstract
How to optimize ranking metrics such as Normalized Discounted Cumulative Gain (NDCG) is an important but challenging problem, because ranking metrics are either flat or discontinuous everywhere, which makes them hard to be optimized directly. Among existing approaches, LambdaRank is a novel algorithm that incorporates ranking metrics into its learning procedure. Though empirically effective, it still lacks theoretical justification. For example, the underlying loss that LambdaRank optimizes for remains unknown until now. Due to this, there is no principled way to advance the LambdaRank algorithm further. In this paper, we present LambdaLoss, a probabilistic framework for ranking metric optimization. We show that LambdaRank is a special configuration with a well-defined loss in the LambdaLoss framework, and thus provide theoretical justification for it. More importantly, the LambdaLoss framework allows us to define metric-driven loss functions that have clear connection to different ranking metrics. We show a few cases in this paper and evaluate them on three publicly available data sets. Experimental results show that our metric-driven loss functions can significantly improve the state-of-the-art learning-to-rank algorithms.
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Diagnostic Evaluation of Policy-Gradient-Based Ranking
Yu, H.-T., Huang, D., Ren, F., & Li, L. (2021) · Electronics · doi:10.3390/electronics11010037 · Cited by 5 · see citing works
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce ModelPreprint
Ma, J., Yi, X., Tang, W., Zhao, Z., Hong, L., Ed, H., & Mei, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05067 · Cited by 5 · see citing works
Learning from User Interactions with Rankings: A Unification of the FieldPreprint
Oosterhuis, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.06576 · Cited by 5 · see citing works
LambdaRank Gradients are Incoherent
Marcuzzi, F., Lucchese, C., & Orlando, S. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614948 · Cited by 4 · see citing works
Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
Shi, W., Wang, C., Feng, F., Zhang, Y., Wang, W., Wu, J., & He, X. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645371 · Cited by 4 · see citing works
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems
Wegmeth, L., Vente, T., & Purucker, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56027-9_9 · Cited by 4 · see citing works
Rank over Class: The Untapped Potential of Ranking in Natural Language Processing
Atapour–Abarghouei, A., Bonner, S., & McGough, A. S. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671386 · Cited by 4 · see citing works
Preference-Based Multi-Robot Planning for Nuclear Power Plant Online Monitoring and Diagnostics
Hesu, A., Kim, S., & Zhang, F. (2023) · Nuclear Science and Engineering · doi:10.1080/00295639.2023.2239635 · Cited by 4 · see citing works
TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition
Barros, T., Garrote, L., Aleksandrov, M. D., Premebida, C., & Nunes, U. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10421819 · Cited by 4 · see citing works
Context-aware Reranking with Utility Maximization for RecommendationPreprint
Xi, Y., Liu, W., Dai, X., Tang, R., Zhang, W., Liu, Q., He, X., & Yu, Y. H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.09059 · Cited by 4 · see citing works
Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval
Mekonnen, K. A., Tang, Y., & de Rijke, M. (2025) · Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3726302.3730023 · Cited by 3 · see citing works
“To address the aforementioned limitations, Zhou et al. [68] propose GenRRL, a generative retrieval model based on RLRF to optimize generative models for alignment with document-level relevance.”
Hazards Prioritization With Cognitive Attention Maps for Supporting Driving Decision-Making
Huang, Y., & Wang, X. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3413675 · Cited by 3 · see citing works
Self-citationNLTSP: A cost model for tensor program tuning using nested loop trees
Qin, X., Li, Y., Lin, F., & Li, W. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103305 · Cited by 3 · see citing works
Breaking the Top- K Barrier: Advancing Top- K Ranking Metrics Optimization in Recommender Systems
Yang, W., Chen, J., Zhang, S., Wu, P., Sun, Y., Feng, Y., Chen, C., & Wang, C. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736866 · Cited by 3 · see citing works
UniCoX: A Unified Cost Model for Tensorized Program Tuning Across Ubiquitous Accelerators
Wang, Z. M., Gong, L., Lou, W., Wang, T., Cheng, Q., Chen, X., Wang, C., & Zhou, X. (2025) · IEEE Transactions on Computers · doi:10.1109/tc.2025.3626148 · Cited by 3 · see citing works
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems
Wang, Y., Wang, Z., Yang, J., Wen, S., Kong, D., Li, H., & Gai, K. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645605 · Cited by 3 · see citing works
Meta-Learning to Rank for Sparsely Supervised Queries
Wu, X., Puthenputhussery, A., Shang, H., Kang, C., & Fang, Y. (2024) · ACM Transactions on Information Systems · doi:10.1145/3698876 · Cited by 3 · see citing works
UniCoMo: A Unified Learning-Based Cost Model for Tensorized Program Tuning
Wang, Z., Gong, L., Lou, W., Cheng, Q., Chen, X., Wang, C., & Zhou, X. (2024) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd63220.2024.00081 · Cited by 3 · see citing works
Beyond Relevance Ranking: A General Graph Matching Framework for Utility-Oriented Learning to Rank
Dai, X., Xi, Y., Zhang, W., Liu, Q., Tang, R., He, X., Hou, J., Wang, Y.-X., & Yu, Y. H. (2021) · ACM Transactions on Information Systems · doi:10.1145/3464303 · Cited by 3 · see citing works
Pande, M., Kakkar, V., Bansal, M., Kumar, S., Sharma, C., Malhotra, H., & Mehta, P. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557113 · Cited by 3 · see citing works
Distributionally robust learning-to-rank under the Wasserstein metric
Sotudian, S., Chen, R., & Paschalidis, I. C. (2023) · PLoS ONE · doi:10.1371/journal.pone.0283574 · Cited by 3 · see citing works
StochasticRank: Global Optimization of Scale-Free Discrete FunctionsPreprint
Ustimenko, A., & Prokhorenkova, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.02122 · Cited by 3 · see citing works
ITNR: Inversion Transformer-based Neural Ranking for Cancer Drug Recommendations
Sotudian, S., & Paschalidis, I. C. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108312 · Cited by 2 · see citing works
Large Scale Hypergraph Computation
Dai, Q., & Gao, Y. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-99-0185-2_8 · Cited by 2 · see citing works
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems
Zhu, Z., Qin, R., Huang, J., Dai, X., Yu, Y., Yu, Y. H., & Zhang, W. (2023) · ACM Transactions on Information Systems · doi:10.1145/3637869 · Cited by 3 · see citing works
Utility-Oriented Reranking with Counterfactual Context
Xi, Y., Liu, W., Dai, X., Tang, R., Liu, Q., Zhang, W., & Yu, Y. H. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3671004 · Cited by 2 · see citing works
Formulating Major League Baseball Playoff Prediction as a Ranking Problem
Zhao, W., Yang, S., & Luo, X. (2024) · International Conference on Pattern Recognition and Intelligent Systems (PRIS) · doi:10.1145/3689218.3689229 · Cited by 2 · see citing works
NeuralLoss: A Learnable Pretrained Surrogate Loss for Learning to Rank
Liu, C., Jiang, C., & Zhou, L. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3562450 · Cited by 2 · see citing works
AdaNDV: Adaptive Number of Distinct Value Estimation via Learning to Select and Fuse Estimators
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Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search
Trimbach, E., Abdallaoui, B., & Missault, P. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3717577 · Cited by 2 · see citing works
Improving Cloud Storage Search with User Activity
Jagerman, R., Kong, W., Pasumarthi, R. K., Qin, Z., Bendersky, M., & Najork, M. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441780 · Cited by 2 · see citing works
Self-citationPersonalized Abstractive Opinion Tagging
Zhao, M., Yang, Y., Li, M., Wang, J., Wu, W. B., Ren, P., de Rijke, M., & Ren, Z. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3532037 · Cited by 2 · see citing works
Explicit and Implicit Semantic Ranking Framework
Zhu, X., Lin, T., Anand, V., Calderwood, M. D., Clausen-Brown, E., Lueck, G., Yim, W.-W., & Wu, C. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3584621 · Cited by 2 · see citing works
Learning to Rank with Variable Result Presentation Lengths
Knyazev, N., & Oosterhuis, H. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730020 · Cited by 1 · see citing works
A Node-Aware Dynamic Quantization Approach for Graph Collaborative Filtering
Li, L., Li, C., Yin, Y., Tao, X., & Zhang, J. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761244 · Cited by 1 · see citing works
Self-citationUniSparTa: A Unified Sparse Tensor Program Tuning Framework
Wang, Z. M., Gong, L., Qu, X., Tang, C. C., Lou, W., Wang, T., Cheng, Q., Chen, X., Wang, C., & Zhou, X. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3636442 · Cited by 1 · see citing works
Guo, Y., & Broucke, S. V. (2026) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-025-01184-6 · Cited by 1 · see citing works
MC-GNNAS-Dock: Multi-criteria GNN-Based Algorithm Selection for Molecular Docking
Cao, S., Wu, H., Wang, J. B., Yuan, Y., & Mısır, M. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7075-1_46 · Cited by 1 · see citing works
Learning to Re-rank with Constrained Meta-Optimal Transport
Hoyos-Idrobo, A. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591714 · Cited by 1 · see citing works
- Authors
- Wang, X., Li, C., Golbandi, N., Bendersky, M., & Najork, M.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the 27th ACM International Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2018-10-17
- Pages
- 1313-1322
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W2898073868
- Cited by
- 136
- References
- 39
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 22 · 2023: 24 · 2022: 20 · 2021: 23 · 2020: 16 · 2019: 6 · 2018: 1
- FWCI
- 9.03
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- learning to rank, NDCG
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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