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14 papers from Meta AI (FAIR) on Recommendation & Information Retrieval
Click intent matters—MARCO achieves nearly perfect calibration and boosts conversions by 2.80% by treating clicks as distinct signals rather than uniform events.
Personalized recommendations can now leverage social network dynamics, leading to a measurable 0.43% increase in user engagement on Meta's platform.
ROCS can triple the throughput of recommendation systems without sacrificing prediction accuracy, revolutionizing how we handle user requests in large-scale settings.
OneShot redefines retrieval efficiency by achieving a 20% recall gain while enhancing user engagement metrics in Instagram's recommendation system.
A simple local filter can outperform complex methods in feature ranking, achieving competitive predictive performance while slashing computational costs.
WHALE unifies non-sequence and sequence feature modeling, achieving superior recommendation performance while maintaining efficiency in industrial settings.
Achieving near-autoregressive accuracy while boosting decoding speed by over 2.4 times could redefine efficiency benchmarks in generative reasoning tasks.
Reasoning-aware retrieval can boost language model performance by surfacing diverse solution strategies that traditional methods overlook.
LLMs can make ad recommendations more stable and predictable, ensuring that minor creative changes don't lead to wild swings in ad delivery.
Storing user interaction histories in a normalized, immutable tier and reconstructing sequences just-in-time slashes data infrastructure costs and unlocks the potential of ultra-long sequence DLRMs.
Forget quadratic complexity: ULTRA-HSTU achieves 21x faster inference and 4-8% better engagement in large-scale recommendation by co-designing input sequences, sparse attention, and model topology.
Achieve zero-collision embedding tables in production recommenders without sacrificing training speed, unlocking better personalization via fresher and higher-quality item embeddings.
Ditch ANN search altogether: MFLI learns a hierarchical index alongside item embeddings, boosting recall by up to 11.8% and cold-content delivery by 57.29% in large-scale recommender systems.