Deep learning for recommender systems
In the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. Nick Pentreath explores recent advances in this area in both research and practice.
| Talk Title | Deep learning for recommender systems |
| Speakers | Nick Pentreath (IBM) |
| Conference | Strata Data Conference |
| Conf Tag | Make Data Work |
| Location | New York, New York |
| Date | September 26-28, 2017 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
In the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. Nick Pentreath explores recent advances in this area in both research and practice. Nick explains how deep learning can be applied to recommendation settings, architectures for handling contextual data, side information, and time-based models, compares deep learning approaches to other cutting-edge contextual recommendation models, and explores scalability issues and model serving challenges.
