Lucchese C., Nardini F. M., Pasumarthi R. K., Bruch S., Bendersky M., Wang X., Oosterhuis H., Jagerman R., De Rijke M.
Deep learning Efficiency/effectiveness trade-off Unbiased learning Learning to rank
This tutorial aims to weave together diverse strands of modern Learning to Rank (LtR) research, and present them in a unified full-day tutorial. First, we will introduce the fundamentals of LtR, and an overview of its various sub-fields. Then, we will discuss some recent advances in gradient boosting methods such as LambdaMART by focusing on their efficiency/effectiveness trade-offs and optimizations. Subsequently, we will then present TF-Ranking, a new open source TensorFlow package for neural LtR models, and how it can be used for modeling sparse textual features. Finally, we will conclude the tutorial by covering unbiased LtR -- a new research field aiming at learning from biased implicit user feedback. The tutorial will consist of three two-hour sessions, each focusing on one of the topics described above. It will provide a mix of theoretical and hands-on sessions, and should benefit both academics interested in learning more about the current state-of-the-art in LtR, as well as practitioners who want to use LtR techniques in their applications.
Source: 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1419–1420, Parigi, Francia, 21/07/2019, 25/07/2019
@inproceedings{oai:it.cnr:prodotti:415713, title = {Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning}, author = {Lucchese C. and Nardini F. M. and Pasumarthi R. K. and Bruch S. and Bendersky M. and Wang X. and Oosterhuis H. and Jagerman R. and De Rijke M.}, doi = {10.1145/3331184.3334824}, booktitle = {42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1419–1420, Parigi, Francia, 21/07/2019, 25/07/2019}, year = {2019} }