Search Results for author:
Found 11 papers, 4 papers with code
Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games
Deck building is a crucial component in playing Collectible Card Games (CCGs).
The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games
The selection of heroes, also known as pick or draft, takes place before the match starts and alternates between the two teams until each player has selected one hero.
Horizon: Facebook's Open Source Applied Reinforcement Learning Platform
In this paper we present Horizon, Facebook's open source applied reinforcement learning (RL) platform.
Band-limited Soft Actor Critic Model
Soft Actor Critic (SAC) algorithms show remarkable performance in complex simulated environments.
Reinforcement Learning-based Product Delivery Frequency Control
We demonstrate statistically significant improvement in daily metrics and resource efficiency by our method in several notification applications at a scale of billions of users.
A Validation Tool for Designing Reinforcement Learning Environments
Reinforcement learning (RL) has gained increasing attraction in the academia and tech industry with launches to a variety of impactful applications and products.
Personalized Execution Time Optimization for the Scheduled Jobs
Scheduled batch jobs have been widely used on the asynchronous computing platforms to execute various enterprise applications, including the scheduled notifications and the candidate pre-computation for the modern recommender systems.
NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
To overcome the data multi-modality and architecture heterogeneity challenges in the recommendation domain, NASRec establishes a large supernet (i. e., search space) to search the full architectures.
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle.
AutoML for Large Capacity Modeling of Meta's Ranking Systems
The large scale of models and tight production schedule requires AutoML to outperform human baselines by only using a small number of model evaluation trials (around 100).
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.
