Adtech
Introducing CLEPR, our model for semantic understanding
Discover CLEPR, Criteo’s deep learning model for semantic search and product retrieval, improving relevance and retail media performance at scale.
AdTech, culture, diversity, and AI: Notes from our latest Women In Tech Meetup
On a weeknight in Bucharest, Criteo opened the doors of its new tech hub for a Women in Tech meetup under a simple but clear theme: Driving Change in Tech. The agenda moved from a deep‑dive keynote on ad tech at scale to a panel about culture, diversity, and AI, and finally, networking. The result…
Leveraging Commerce Data for Outcome-Based Relevancy in Agentic Recommendation Systems
Learn how commerce data improves outcome-based relevance in agentic recommendation systems, delivering 37% better retrieval and 60% better SKU re-ranking.
Video Ads: Introducing video interactivity in production
At the end of 2024, Criteo’s Video R&D team started working on a new goal: adding recommended products on top of videos, so that each display delivered to someone on the Open Web would become the most relevant possible. Over the last two decades, Criteo has continuously advanced its recommendation engine. Good news: From now…
Off-Policy Learning in Large Action Spaces
Optimization Matters More Than Estimation This Research Card shows that in large-scale recommendation systems, like those used at Criteo, focusing on how we optimize algorithms is more impactful than trying to perfectly estimate outcomes. By using simpler, more stable training objectives, we can achieve better click-through rates, faster experimentation, and more scalable performance across millions…
Criteo Boosts Ad Performance with Latest AI Advancements
Abstract. Everyone talks about AI today, and it seems AI has become an off-the-shelf tool providing plug-and-play capabilities. However, when addressing the hardest problems, such as those arising in the Commerce Media and AdTech industry, things are far more complex and require advanced AI (more specifically, machine learning) expertise. In this post, we share the…
How RecSys & LLMs Will Converge: Architecture of Hybrid RecoAgents
Recommender systems have become critical infrastructure in commerce. Their role is not only to surface relevant items but also to shape how users explore, compare, and decide. Improvements in recommendation translate directly into superior business outcomes: higher engagement, more conversions, stronger loyalty, and more efficient allocation of marketing budgets. Over the last couple of years,…
Agents, APIs, and Advertising: Lessons From Engineering Our MCP Server
MCP, Model Context Protocol, has made quite some noise in the past year as it promises a simpler, user-driven, integration of tools into Large Language Models (LLMs). At Criteo, we believe that MCP will be key to empowering our clients and giving them the control to create and manage advertising campaigns in a new way:…
Research Card: FairJob – A Real-World Dataset for Fairness in Online Systems
Research Card on how to make the outcome prediction in online systems fairer? Title: FairJob: A Real-World Dataset for Fairness in Online Systems Short Title: Discover and mitigate bias in advertising Authors: Mariia Vladimirova (Criteo AI Lab), Federico Pavone (Paris-Dauphine), Eustache Diemert (Criteo AI Lab) Team: Research.FDL, collaboration with Paris-Dauphine University Revue: NeurIPS 2024, datasets and…
Research Card: Fixed Point Label Attribution for Real-Time Bidding
Research Card on how to attribute positive outcomes to touchpoints in a sequence of displays leading to a conversion. Paper: Fixed Point Label Attribution for Real-Time BiddingAuthors: Martin Bompaire, Antoine Désir, Benjamin HeymannCategory: Operations ResearchRevue: Manufacturing and Service Operations Management Why did we work on this topic (the problem we want to solve)? In display advertising,…
