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Guillaume Allain - Recommender systems with Tensorflow

PyData London 2017 Description 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. Abstract 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. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. We aim to be an accessible, community-driven conference, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...