Reco in the air

It’s been almost a year since we met in Boston in 2016, so we thought now might be a good time to give you some fresh news about the team as well as matter for discussions when you come see us in Como in August!

In case you’re new to Product Recommendation at Criteo, here is a post that will help you get the gist of it.

Reco in the air

First of all, we are excited to announce that two of our papers have been accepted at the RecSys Deep Learning workshop this year! Congrats to Elena Smirnova, Thomas Nedelec and Flavian Vasile for their hard work!

• Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks, Elena Smirnova, Flavian Vasile
• Specializing Joint Representations for the task of Product Recommendation, Thomas Nedelec, Elena Smirnova, Flavian Vasile

This work is making its way to Production and will be AB tested soon!

We have also been very active leading the French chapter of RecSys meetups. The  latest edition was in June and featured presentations by Simon Lefebvre (Antvoice), Olivier Grisel (INRIA) and Robbert van Der Pluijm (Bibblio Labs). Our next session will be in September and will be hosted by tinyClues.  Stay tuned!

Recently our very own Simon Dolle went to  Berlin Buzzword to give a presentation of our work on Word2Vec applied to product recommendation.

We will also be around at the  2nd  RecSys meetup in London where Olivier Koch will be presenting our latest work on Reco at scale.

Finally, our  public dataset on large-scale counterfactual learning is making its way out, make sure to take a look at it.

A few challenges we are looking at

After a couple years of intense work on  Word2Vec applied to product recommendation, our first version has finally made its way to production. But this is just the beginning. New versions are coming, adding content metadata and more scalability.

Here are a few other hard problems we are tackling:

• Advanced user representation: can we leverage contextual sequence modeling to build more personalized recommendations at scale?
• Causality and attribution: moving forward beyond last-click attribution, can we make recommendation better by building a better understanding of the causality between displays, clicks and sales?
• Vectorized reco to the next level: can we make use of deep learning to build better representations of our users and products?
• Catalog enrichment: can we build a generic catalog representation that would allow us to make more sense of the events we see online?
• New machine learning models: RNNs and LSTMs are hot these days. Can they really make a difference at scale for billions of products and users?
• Evaluating recommender systems over time: AB testing is great but costly. How can we make the best of our AB test slots? How can we make evaluate the long-term effects of a new model in production?

These topics are being addressed in a deeply collaborative way by our machine learning engineers and research scientists.  We use state-of-the-art open technologies (Tensorflow, Spark, Hadoop, python notebooks) and share back with the community every time we can.

Join us!

If you feel excited by these challenges, the Reco team at Criteo offers vast opportunities for machine learning engineers and scientists. The team is growing. We will welcome two new hires this summer and have more open positions in Paris and Palo Alto!

Make sure to  apply if you are interested! Even better, come talk to us at RecSys in Como!

The Criteo Reco Team, (left to right): Aurel Ghioca, Olivier Koch, Flavian Vasile, Lowik Chanussot, Amine Benhalloum, Alexandre Abraham, Vincent Latrouite, Dmitry Otroshchenko, JP Lam Yee Mui

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