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A self-evolving memory paradigm for generative recommendation is proposed, aiming to enable effective evolution across heterogeneous behavioral patterns, and three key principles for effective self-evolving recommendation systems are identified, including isolated memorization, reinforced evolution, and scalable application.
Ditch ANN search altogether: MFLI learns a hierarchical index alongside item embeddings, boosting recall by up to 11.8% and cold-content delivery by 57.29% in large-scale recommender systems.