  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.
