✈️2nd release TF-Ranking by Google AI
In December 2018, Google AI introduced TF-Ranking, an open-source library based on TensorFlow for developing scalable neural ranking models (LTR, learning-to-rank) that help get an ordered list of items in response to a user queries. Unlike standard classification models, which classify one item at a time, LTR models take a complete list of items as input and look for an order that maximizes the usefulness of the entire list. These LTR models are most common in search and recommendation systems, but TF-Ranking is also used in e-commerce, building smart spaces and cities.
In May 2021, Google AI released its second TF-Ranking release, which provides full support for built-in LTR model building using Keras, the high-level TensorFlow 2 API. The Keras ranking model has a new workflow design, incl. flexible ModelBuilder and DatasetBuilder for customizing the training set, and a pipeline for training the model. Also this version of TF-Ranking supports RaggedTensors, Orbit training library and many more improvements.
And thanks to an in-depth study of the capabilities of the TF-Ranking library, the Google AI team has created a Data Augmented Self-Attentive Latent Cross (DASALC) model that combines transformation of neural network features with data enrichment, ensemble methods, and loss ranking. DASALC eliminates the disadvantages of LTR models and gradient boosting decision trees, while retaining the advantages of these methods.
https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html
https://research.google/pubs/pub50030/