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Mining high-quality samples from CTR data can dramatically enhance multimodal representation learning, leading to superior prediction accuracy.
Re-ranking control alone boosts key performance metrics by over 2%, but extending it to fine ranking unlocks even greater gains without sacrificing system stability.
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.
LLM-based recommendation gets a boost: initializing item embeddings with semantic keywords and aligning tokens to item clusters significantly improves performance.
