Every day, Meta’s recommendation platforms handle billions of user interactions, generating rich temporal signals that capture individual preferences and intent across products, ads, and content. In our 2024 post on sequence learning for ads recommendations, we showed how modeling the order and timing of user actions (rather than relying on static, manually engineered sparse features) [...]
The post From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking appeared first on Engineering at Meta.
source & further reading
engineering.fb.com — original article
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