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Future interactions can be harnessed to significantly boost recommendation accuracy without compromising inference efficiency.
By cutting end-to-end serving resource consumption by over 50% while boosting user engagement metrics, RecGPT-V3 redefines efficiency in large-scale recommender systems.
OneRank achieves superior multi-task recommendation performance by seamlessly integrating task-specific learning within a unified Transformer framework, eliminating the traditional encoder-predictor bottleneck.
Train smarter, not bigger: LoopCTR unlocks state-of-the-art CTR prediction by decoupling computation from parameter growth through recursive layer reuse.
