Advances in TF-Ranking

Posted by Michael Bendersky and Xuanhui Wang, Software Engineers, Google Research

In December 2018, we introduced TF-Ranking, an open-source TensorFlow-based library for developing scalable neural learning-to-rank (LTR) models, which are useful in settings where users expect to receive an ordered list of items in response to their query. LTR models — unlike standard classification models that classify one item at a time — receive an entire list of items as an input, and learn an ordering that maximizes the utility of the entire list. While search and recommendation systems are the most common applications of LTR models, since its release, we have seen TF-Ranking being applied in diverse domains beyond search, including e-commerce, SAT solvers, and smart city planning.

The goal of learning-to-rank (LTR) is to learn a function f() that takes as an input a list of items (documents, products, movies, etc.) and outputs the list of items in the optimal order (descending order of relevance). Here, green shade indicates item relevance level, and the red item marked with ‘x’ is non-relevant.

In May 2021, we published a major release of TF-Ranking that enables full support for natively building LTR models using Keras, a high-level API of TensorFlow 2. Our native Keras ranking model has a brand-new workflow design, including a flexible ModelBuilder, a DatasetBuilder to set up training data, and a Pipeline to train the model with the provided dataset. These components make building a customized LTR model easier than ever, and facilitate rapid exploration of new model structures for production and research. If RaggedTensors are your tool of choice, TF-Ranking is now working with them as well. In addition, our most recent release, which incorporates the Orbit training library, contains a long list of advances — the culmination of two and half years of neural LTR research. Below we share a few of the key improvements available in the latest TF-Ranking version.

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