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Found 4 papers, 2 papers with code
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta
To address these challenges, we present Scaling User Modeling (SUM), a framework widely deployed in Meta's ads ranking system, designed to facilitate efficient and scalable sharing of online user representation across hundreds of ads models.
Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking
In order to pass better ads from the early to the final stage ranking, we propose a multi-task learning framework for early stage ranking to capture multiple final stage ranking components (i. e. ads clicks and ads quality events) and their task relations.
How to Build User Simulators to Train RL-based Dialog Systems
We propose a method of standardizing user simulator building that can be used by the community to compare dialog system quality using the same set of user simulators fairly.
Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good
Developing intelligent persuasive conversational agents to change people's opinions and actions for social good is the frontier in advancing the ethical development of automated dialogue systems.
