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ReliGRec (Reliability-oriented Generative Recommendation), a weakly supervised framework whose name denotes its design goal rather than a supervised reliability variable, is proposed, which turns weak-risk estimation from an auxiliary prediction into a generation-time control signal.
OneModel boosts user engagement by over 1% while simplifying the engineering complexity of multi-scenario ranking systems.
HiLaR reveals that optimizing user preference modeling through hierarchical latent reasoning can significantly outperform traditional recommendation approaches.
