Criteolabs goes to ACM Recsys 2015, Vienna, Austria.

When: Wed 16 – Sun 20 September

Location: Technische Univerität Wien, Vienna, Austria

Criteolabs is happy to be a Gold Sponsor of the 9^th installment of ACM Recsys 2015, which takes place at the Technische Univerität Wien, Vienna, Austria from September 16-20, 2015.

The ACM Recommender Systems conference (RecSys) is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. Recommendation is a particular form of information filtering, that exploits past behaviors and user similarities to generate a list of information items that is personally tailored to an end-user’s preferences.

This event will bring together researchers and practitioners from academia and industry to present their latest results and identify new trends and challenges in providing recommendation components in a range of innovative application contexts.

Criteolabs’ Romain Lerallut presents: Large-Scale Real-Time Product Recommendation At Criteo

Bio : Romain is a Senior Engineering Manager at Criteo in the Engine department, in charge of applying large-scale machine learning algorithms to actual problems such as product recommendation or graphical layout optimization. Romain has been working on building up the Engine since 2011, first as devlead and then as manager. Before joining Criteo, he was taught computers how to read cursive handwriting at A2iA and holds an engineering degree from “Ecole des Ponts-Paristech” and a PhD in Computer Science from “Ecoles des Mines-Paristech”.

Abstract: Performance retargeting consists of displaying online advertisements that are personalized according to each user’s browsing history. Criteo’s recommender system chooses a dozen relevant products from over two billion candidates in a few milliseconds, not only for their click performance but also for their probability to generate a sale.

This talk takes place on Friday September 18 from 2pm – 4 pm in HS 1. This session will expose how to build such a system through a combination of offline and real-time computations, and the challenges of evolving it when regular A/B testing no longer suffices.