4 citations · 4 across the 3 of their papers we have counts for
6 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…
Neural Optimization with Adaptive Heuristics for Intelligent Marketing System
Changshuai Wei, Benjamin Zelditch, Joyce Chen +6
Computational marketing has become increasingly important in today's digital world, facing challenges such as massive heterogeneous data, multi-channel customer journeys, and limit…