"These models are trained to optimize for user positive engagement such as click-through-rate (CTR) - the probability of a user clicking a notification - as well as other metrics like time spent. However, while engagement-optimized models are effective at driving interactions, there's a risk that they might overprioritize the product types and authors someone has previously engaged with."
"We've introduced a diversity-aware notification ranking framework that helps deliver more diverse, better curated, and less repetitive notifications. This framework has significantly reduced daily notification volume while improving CTR. The diversity layer evaluates each notification candidate's similarity to recently sent notifications across multiple dimensions such as content, author, notification type, and product surface. It then applies carefully calibrated penalties to downrank candidates that are too similar or repetitive."
Machine learning models decide notification recipients, timing, and content, optimizing for engagement metrics like click-through rate and time spent. Engagement-focused optimization can overemphasize previously engaged product types and authors, causing repetitive exposure and reduced discovery. A diversity-aware notification ranking framework evaluates each candidate's similarity to recently sent notifications across dimensions such as content, author, notification type, and product surface. The framework applies calibrated penalties to downrank overly similar or repetitive candidates, producing more diverse, better curated notifications. Deployment of this framework reduced daily notification volume while simultaneously improving CTR.
Read at Social Media Today
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