recommendation-systems.
7 writings found
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Meta's Multi-Stage Sequence Learning: What It Means for Recommendation Systems
Meta achieves LLM-style scaling laws in ads recommendations through a two-stage sequence model. Here's what this architecture teaches us about scaling complex ML systems.
Meta's Hierarchical Interest Representation: A Shift in Graph-Based Ranking
How Meta's new representation layer tackles sparse signals in deep funnel ads using hierarchical clustering, multimodal features, and efficient transformers.
Meta's Hierarchical Interest Representation: A New Approach to Graph-Scale ML
Exploring Meta's breakthrough in recommendation systems that combines sparse engagement signals with world knowledge to power ads across billions of users.
Meta's Adaptive Ranking Model: The Real Cost of Serving Trillion-Parameter Ads
Meta scaled ads recommendations to LLM complexity while keeping latency under a second. Here's why their inference trilemma solution matters beyond advertising.
How Facebook Built Friend Bubbles: A Deep Dive into Social ML Architecture
Meta's friend bubbles system combines closeness prediction models, ranking optimization, and performance engineering to surface friend-driven content at scale.
Facebook's Friend Bubbles: When Social Graphs Meet Recommendation Systems
Meta's friend bubbles on Reels reveal how social signals and ML models can coexist in video recommendations without destroying performance.
Facebook's Friend Bubbles: A Masterclass in Social Graph ML
How Meta blends closeness prediction models, multi-task ranking, and prefetch optimization to surface friend-driven content at scale on Reels