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TF-Ranking: A Scalable TensorFlow Library for Learning-to-Rank

Wednesday, December 5, 2018

Posted by Xuanhui Wang and Michael Bendersky, Software Engineers, Google AImachine translationdialogue systemscomputational biology.learning-to-rank—TensorFlowTF-Rankingpaperpairwise or listwise loss functionsmulti-item scoringranking metric optimizationunbiased learning-to-rankExisting Algorithms and Metrics Supportloss functionprior workembeddingsMean Reciprocal RankNormalized Discounted Cumulative GainTensorBoard

An example of the NDCG metric (Y-axis) along the training steps (X-axis) displayed in the TensorBoard. It shows the overall progress of the metrics during training. Different methods can be compared directly on the dashboard. Best models can be selected based on the metric.

Multi-Item Scoringscored jointly

The TF-Ranking library supports multi-item scoring architecture, an extension of traditional single-item scoring.

recent workLETORRanking Metric OptimizationArea Under the Curvediscontinuous or flatrecent work,expectation-maximizationUnbiased Learning-to-RankPrior researchunbiased learning-to-rankGetting Started with TF-RankingTensorFlow EstimatorTensorboardTensorFlow ServingGitHub repotutorial examplesAcknowledgementsThis project was only possible thanks to the members of the core TF-Ranking team: Rama Pasumarthi, Cheng Li, Sebastian Bruch, Nadav Golbandi, Stephan Wolf, Jan Pfeifer, Rohan Anil, Marc Najork, Patrick McGregor and Clemens Mewald‎. We thank the members of the TensorFlow team for their advice and support: Alexandre Passos, Mustafa Ispir, Karmel Allison, Martin Wicke, and others. Finally, we extend our special thanks to our collaborators, interns and early adopters: Suming Chen, Zhen Qin, Chirag Sethi, Maryam Karimzadehgan, Makoto Uchida, Yan Zhu, Qingyao Ai, Brandon Tran, Donald Metzler, Mike Colagrosso, and many others at Google who helped in evaluating and testing the early versions of TF-Ranking.

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TF-Ranking: A Scalable TensorFlow Library for Learning-to-Rank

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