1. TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter
- Author
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Cai, Vanessa, Prabakar, Pradeep, Rebuelta, Manuel Serrano, Rosen, Lucas, Monti, Federico, Janocha, Katarzyna, Lazovich, Tomo, Raj, Jeetu, Shrinivasan, Yedendra, Li, Hao, and Markovich, Thomas
- Subjects
Computer Science - Information Retrieval ,Computer Science - Machine Learning - Abstract
Recommendation systems are a core feature of social media companies with their uses including recommending organic and promoted contents. Many modern recommendation systems are split into multiple stages - candidate generation and heavy ranking - to balance computational cost against recommendation quality. We focus on the candidate generation phase of a large-scale ads recommendation problem in this paper, and present a machine learning first heterogeneous re-architecture of this stage which we term TwERC. We show that a system that combines a real-time light ranker with sourcing strategies capable of capturing additional information provides validated gains. We present two strategies. The first strategy uses a notion of similarity in the interaction graph, while the second strategy caches previous scores from the ranking stage. The graph based strategy achieves a 4.08% revenue gain and the rankscore based strategy achieves a 1.38% gain. These two strategies have biases that complement both the light ranker and one another. Finally, we describe a set of metrics that we believe are valuable as a means of understanding the complex product trade offs inherent in industrial candidate generation systems., Comment: 10 pages, 3 figures
- Published
- 2023