Contribute Media
A thank you to everyone who has made this possible: Read More

Neural Networks for Recommender Systems

Description

PyData Amsterdam 2017

Neural networks are quickly becoming the tool of choice for recommender systems. In this talk, I'm going to present a number of neural network recommender models: from simple matrix factorization, through learning-to-rank, to recurrent architectures for sequential prediction. All my examples are accompanied by links to implementations to give a starting point for further experimentation.

The versatility and representational power of artificial neural networks is quickly making them the preferred tool for many machine learning tasks. The same is true of recommender systems: neural networks allow us to quickly iterate over new models and to easily incorporate new user, item, and contextual features. In this talk, I'm going to present a number of useful architectures: from simple matrix factorization in neural network form, through learning-to-rank models, to more complex recurrent architectures for sequential prediction. All my examples are accompanied by links to implementations to provide a starting point for further experimentation.

Details

Improve this page