ABSTRACT
Learning-to-Rank is a branch of supervised machine learning that seeks to produce an ordering of a list of items such that the utility of the ranked list is maximized. Unlike most machine learning techniques, however, the objective cannot be directly optimized using gradient descent methods as it is either discontinuous or flat everywhere. As such, learning-to-rank methods often optimize a loss function that either is loosely related to or upper-bounds a ranking utility instead. A notable exception is the approximation framework originally proposed by Qin et al. that facilitates a more direct approach to ranking metric optimization. We revisit that framework almost a decade later in light of recent advances in neural networks and demonstrate its superiority empirically. Through this study, we hope to show that the ideas from that work are more relevant than ever and can lay the foundation of learning-to-rank research in the age of deep neural networks.
- Mart'in Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et almbox. 2016. Tensorflow: a system for large-scale machine learning. In Proc. of the 12th USENIX Symposium on Operating Systems Design and Implementation. 265--283. Google ScholarDigital Library
- Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005. Learning to rank using gradient descent. In Proc. of the 22nd International Conference on Machine Learning. 89--96. Google ScholarDigital Library
- Christopher J.C. Burges. 2010. From RankNet to LambdaRank to LambdaMART: An Overview. Technical Report Technical Report MSR-TR-2010--82. Microsoft Research.Google Scholar
- Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007. Learning to rank: from pairwise approach to listwise approach. In Proc. of the 24th International Conference on Machine Learning. 129--136. Google ScholarDigital Library
- Olivier Chapelle and Yi Chang. 2011. Yahoo! learning to rank challenge overview. In Proc. of the Learning to Rank Challenge. 1--24. Google ScholarDigital Library
- Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics, Vol. 29, 5 (2001), 1189--1232.Google ScholarCross Ref
- Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proc. of the 32nd International Conference on Machine Learning (ICML). 448--456. Google ScholarDigital Library
- Kalervo J"arvelin and Jaana Kekalainen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, Vol. 20, 4 (2002), 422--446. Google ScholarDigital Library
- Thorsten Joachims. 2006. Training linear SVMs in linear time. In Proc. of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 217--226. Google ScholarDigital Library
- Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems 30. 3146--3154. Google ScholarDigital Library
- Donald A Metzler, W Bruce Croft, and Andrew Mccallum. 2005. Direct maximization of rank-based metrics for information retrieval. CIIR report 429. University of Massachusetts.Google Scholar
- Rama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang, Cheng Li, Michael Bendersky, Marc Najork, Jan Pfeifer, Nadav Golbandi, Rohan Anil, and Stephan Wolf. 2019. TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (to appear). Google ScholarDigital Library
- Tao Qin and Tie-Yan Liu. 2013. Introducing LETOR 4.0 Datasets. (2013). arxiv: 1306.2597Google Scholar
- Tao Qin, Tie-Yan Liu, and Hang Li. 2010. A general approximation framework for direct optimization of information retrieval measures. Information Retrieval, Vol. 13, 4 (2010), 375--397. Google ScholarDigital Library
- Michael Taylor, John Guiver, Stephen Robertson, and Tom Minka. 2008. SoftRank: Optimizing Non-smooth Rank Metrics. In Proc. of the 1st International Conference on Web Search and Data Mining. 77--86. Google ScholarDigital Library
- Xuanhui Wang, Cheng Li, Nadav Golbandi, Michael Bendersky, and Marc Najork. 2018. The LambdaLoss Framework for Ranking Metric Optimization. In Proc. of the 27th ACM International Conference on Information and Knowledge Management. 1313--1322. Google ScholarDigital Library
- Qiang Wu, Christopher JC Burges, Krysta M Svore, and Jianfeng Gao. 2010. Adapting boosting for information retrieval measures. Information Retrieval, Vol. 13, 3 (2010), 254--270. Google ScholarDigital Library
- Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. 2008. Listwise approach to learning to rank: theory and algorithm. In Proc. of the 25th International Conference on Machine Learning. 1192--1199. Google ScholarDigital Library
- Jun Xu and Hang Li. 2007. AdaRank: A Boosting Algorithm for Information Retrieval. In Proc. of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 391--398. Google ScholarDigital Library
Index Terms
- Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks
Recommendations
Metric-agnostic Ranking Optimization
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalRanking is at the core of Information Retrieval. Classic ranking optimization studies often treat ranking as a sorting problem with the assumption that the best performance of ranking would be achieved if we rank items according to their individual ...
Quality-biased ranking for queries with commercial intent
WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide WebModern search engines are good enough to answer popular commercial queries with mainly highly relevant documents. However, our experiments show that users behavior on such relevant commercial sites may differ from one to another web-site with the same ...
New Insights into Metric Optimization for Ranking-based Recommendation
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information RetrievalDirect optimization of IR metrics has often been adopted as an approach to devise and develop ranking-based recommender systems. Most methods following this approach (e.g. TFMAP, CLiMF, Top-N-Rank) aim at optimizing the same metric being used for ...
Comments