This talk will demonstrate how to harness a deep-learning framework such as Tensorflow, together with the usual suspects such as Pandas and Numpy, to implement recommendation models for news and classified ads.
Recommender systems are used across the digital industry to model users' preferences and increase engagement. Popularised by the seminal Netflix prize, collaborative filtering techniques such as matrix factorisation are still widely used, with modern variants using a mix of meta-data and interaction data in order to deal with new users and items. We will demonstrate how to implement a variety of models using Tensorflow, from simple bi-linear models expressed as shallow neural nets to the latest deep incarnations of Amazon DSSTNE and Youtube neural networks. We will also use TensorBoard and particularly the embedding projector to visualise the latent space for items and metadata.