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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.
Action-only predictions miss the complexity of user preferences, but DASH captures decision-making processes for more accurate simulations in advertising.
