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 9 parts as described below. For more details, see the overview paper.
Time slot | Topic | Presenters | |
---|---|---|---|
09:30 - 10:15 | Introduction & preliminaries | Tom Kenter |
section slides 1 section slides 2 |
10:15 - 11:00 | Semantic matching | Christophe Van Gysel | section slides |
11:00 - 11:30 | Coffee break | ||
11:30 - 12:15 | Learning to rank | Bhaskar Mitra | section slides |
12:15 - 13:00 | Entities | Tom Kenter, Christophe Van Gysel | section slides |
13:00 - 14:30 | Lunch break | ||
14:30 - 15:15 | Modeling user behavior | Maarten de Rijke | section slides |
15:15 - 16:00 | Generating Responses | Tom Kenter | section slides |
16:00 - 16:30 | Coffee break | ||
16:30 - 17:15 | Recommender systems | Maarten de Rijke | section slides |
17:15 - 17:45 | Industry insights | Tom Kenter, Bhaskar Mitra | section slides |
17:45 - 18:30 | Q + A | Tom Kenter, Christophe Van Gysel, Maarten de Rijke, Bhaskar Mitra |
If you wish to refer to the tutorial in your scientific publication, please refer to our overview paper:
@inproceedings{Kenter2018nn4irecir,
Author = {Kenter, Tom and Borisov, Alexey and Van Gysel, Christophe and Dehghani, Mostafa and de Rijke, Maarten and Mitra, Bhaskar},
Booktitle = {ECIR 2018},
Organization = {Springer},
Title = {Neural Networks for Information Retrieval},
Year = {2018}}