Deep learning for recommender systems
The success of deep learning has reached the realm of structured data in the past few years, where neural networks have been shown to improve the effectiveness and predictability of recommendation engines. Oliver Gindele offers a brief overview of such deep recommender systems and explains how they can be implemented in TensorFlow.
|Talk Title||Deep learning for recommender systems|
|Speakers||Oliver Gindele (Datatonic)|
|Conference||Strata Data Conference|
|Conf Tag||Making Data Work|
|Location||London, United Kingdom|
|Date||April 30-May 2, 2019|
Recommender systems are widely used by ecommerce and services companies worldwide to provide the most relevant items to their users. Over the past few years, deep learning has demonstrated breakthrough advances in image recognition and natural language processing. Meanwhile, new approaches have been published that apply deep learning techniques to recommender systems, further expanding the use cases of neural networks. Some of these novel systems already display state-of-the-art performance and deliver high-quality recommendations. Compared to traditional models, deep learning solutions can provide a better understanding of user’s demands, item’s characteristics, and the historical interactions between them. Oliver Gindele explains how to implement some of these novel models in the machine learning framework TensorFlow, starting from a collaborative filtering approach and extending that to more complex deep recommender systems. TensorFlow can do more than vision or translation. High-level APIs make model building and training painless; custom algorithms and specific loss functions are easily implemented; deep recommender systems work well on real data; and embeddings and hidden layers allow for many ways to improve a recommender system.