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About this paper
Rama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang, Cheng Li, Michael Bendersky, Marc Najork, Jan Pfeifer, Nadav Golbandi and 2 more
Google introduces TF-Ranking, a scalable TensorFlow library designed for large-scale Learning-to-Rank problems. The library enables effective training of deep neural networks for ranking on massive datasets, demonstrating improved ranking quality on Google's production systems like Gmail Search by leveraging listwise losses and integrating sparse textual features.
Key insights
- Listwise loss functions consistently outperform pointwise and pairwise losses for optimizing ranking quality in large-scale search and recommendation tasks.
- Integrating sparse textual features through deep learning embeddings significantly enhances ranking effectiveness in information retrieval systems.
- A scalable deep learning framework is essential for handling massive datasets and complex features inherent in modern industrial Learning-to-Rank applications.
Summary by alphaXiv.
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