Machine learning plays an important role in many aspects of modern IR systems, and deep learning is applied to all of those. The fast pace of modern-day research into deep learning has given rise to many different approaches to many different IR problems.
The tutorial is organized in 8 parts as described below. For more details, see the overview paper on arXiv.
Time slot | Topic | Presenters | |
---|---|---|---|
09:00 - 09:05 | Introduction | Tom Kenter | section slides |
09:05 - 10:00 | Preliminaries | Tom Kenter, Maarten de Rijke | section slides |
10:00 - 10:30 | Text Matching I | Alexey Borisov, Bhaskar Mitra | section slides |
10:30 - 10:50 | Coffee break | ||
10:50 - 11:20 | Text Matching I (cont'd) | Alexey Borisov, Bhaskar Mitra | section slides |
11:20 - 12:20 | Text Matching II | Mostafa Deghani, Christophe Van Gysel | section slides |
12:20 - 14:00 | Lunch break | ||
14:00 - 14:45 | Learning to Rank | Christophe Van Gysel, Bhaskar Mitra | section slides |
14:45 - 15:30 | Modeling User Behavior | Alexey Borisov, Maarten de Rijke | section slides |
15:30 - 15:50 | Coffee break | ||
15:50 - 16:35 | Generating Responses | Mostafa Deghani, Tom Kenter | section slides |
16:35 - 17:20 | Wrap Up | All presenters | section slides |
If you wish to refer to the tutorial in your scientific publication, please refer to our overview paper:
@inproceedings{Kenter2017nn4ir,
Author = {Kenter, Tom and Borisov, Alexey and Van Gysel, Christophe and Dehghani, Mostafa and de Rijke, Maarten and Mitra, Bhaskar},
Booktitle = {SIGIR 2017},
Organization = {ACM},
Pages = {1403--1406},
Title = {Neural Networks for Information Retrieval},
Year = {2017}}