Name
Abstract | ||
|---|---|---|
TensorFlow Ranking is the first open source library for solving large-scale ranking problems in a deep learning framework. It is highly configurable and provides easy-to-use APIs to support different scoring mechanisms, loss functions and evaluation metrics in the learning-to-rank setting. Our library is developed on top of TensorFlow and can thus fully leverage the advantages of this platform. For example, it is highly scalable, both in training and in inference, and can be used to learn ranking models over massive amounts of user activity data. We empirically demonstrate the effectiveness of our library in learning ranking functions for large-scale search and recommendation applications in Gmail and Google Drive. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3292500.3330677 | Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining |
Keywords | Field | DocType |
information retrieval, learning-to-rank, machine learning, recommender systems | Recommender system,Learning to rank,Question answering,Ranking,Information retrieval,Inference,Computer science,Document summarization,Artificial intelligence,Deep learning,Machine learning,Scalability | Journal |
Volume | ISBN | Citations |
abs/1812.00073 | 978-1-4503-6201-6 | 9 |
PageRank | References | Authors |
0.49 | 33 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rama Kumar Pasumarthi | 1 | 16 | 2.25 |
| Xuanhui Wang | 2 | 1394 | 68.85 |
| Cheng Li | 3 | 126 | 7.81 |
| Sebastian Bruch | 4 | 9 | 0.82 |
| Michael Bendersky | 5 | 986 | 48.69 |
| Marc A. Najork | 6 | 2538 | 278.16 |
| jan pfeifer | 7 | 27 | 2.35 |
| Nadav Golbandi | 8 | 436 | 18.68 |
| Rohan Anil | 9 | 359 | 12.55 |
| Stephan Wolf | 10 | 9 | 0.49 |