5 papers
From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation
Changshuai Wei, John Bencina, Phuc Nguyen +2
Large-scale targeting and recommendation systems are typically built around predictive scores fed into heuristic or local allocation. When the business goal is incremental impact,…
Quantizing Intent: Cross-Domain Semantic IDs from Organic Activity for Industrial Ranking
Julie Choi, Haoran Ye, Zhiwei Ding +3
Ads click-through rate (CTR) prediction is constrained by sparse user supervision: most users engage with ads infrequently while generating dense behavioral evidence in organic sur…
An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking
Lars Hertel, Gaurav Srivastava, Syed Ali Naqvi +21
LinkedIn Feed enables professionals worldwide to discover relevant content, build connections, and share knowledge at scale. We present Feed Sequential Recommender (Feed SR), a tra…
BanditLP: Large-Scale Stochastic Optimization for Personalized Recommendations
Phuc Nguyen, Benjamin Zelditch, Joyce Chen +2
We present BanditLP, a scalable multi-stakeholder contextual bandit framework that unifies neural Thompson Sampling for learning objective-specific outcomes with a large-scale line…
Beyond Basic A/B testing: Improving Statistical Efficiency for Business Growth
Changshuai Wei, Phuc Nguyen, Benjamin Zelditch +1
The standard A/B testing approaches are mostly based on t-test in large scale industry applications. These standard approaches however suffers from low statistical power in busines…