Equip Pre-ranking with Target Attention by Residual Quantization
Researchers have developed TARQ, a new pre-ranking framework for recommendation systems that aims to improve efficiency and effectiveness. TARQ incorporates a Target Attention-like architecture through Residual Quantization, bringing advanced modeling capabilities to the pre-ranking stage. This innovation has been successfully deployed on Taobao, serving millions of users and demonstrating significant performance gains. AI
IMPACT Enhances recommendation system efficiency and effectiveness, potentially improving user experience and business outcomes.