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Expectation-consistency in recommender systems can be achieved without sacrificing performance, thanks to the PIT-SUN framework's innovative approach to empirical marginal recovery.
A novel approach that boosts LTV prediction for billions of low-activity users by transforming sparse profiles into actionable insights without heavy LLM reliance.
Retention models can now harness the power of post-conversion content without risking feature leakage, leading to more accurate predictions of user engagement.
UniMixer achieves state-of-the-art scaling in recommendation systems by unifying disparate architectures into a single framework that learns optimal token mixing patterns.
