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This paper addresses the challenge of Zero-Observation Reactivation in sequential recommendation systems, where users return after long periods of inactivity without any observed behavior. By introducing DeltaGate, a lightweight output-layer plugin that dynamically routes representation dimensions based on the duration of user inactivity, the authors demonstrate significant improvements in recommendation accuracy on three Amazon datasets. The results show that DeltaGate can outperform traditional models like SASRec and BERT4Rec, achieving higher Hit@10 scores while maintaining a minimal increase in trainable parameters.
DeltaGate achieves a remarkable boost in recommendation accuracy for returning users after long inactivity, outperforming established models with significantly fewer parameters.
Sequential recommendation (SR) models capture continuously observed behavior, but a returning user may have no interactions for months or years. We define this setting as Zero-Observation Reactivation: the user has a pre-gap history, while the platform observes no behavioral signals during a macro-gap Delta t. Under a chronologically aligned Gap-Synthesize Protocol on three Amazon datasets (Video Games, CDs&Vinyl, and Movies&TV), Hit@10 decreases monotonically across the evaluated gap buckets and reaches its lowest level beyond one year. The pattern appears across recurrent, unidirectional, and bidirectional SR backbones. We propose DeltaGate, a lightweight output-layer plugin that keeps the backbone frozen and routes each representation dimension between the personalized history and a learned, zero-initialized global prior. The gate is conditioned jointly on Delta t and the personalized representation. In a controlled diagnostic, we hold the personalized representation fixed and vary Delta t to isolate the trained gate's response to the gap input. In the>365d Video Games bucket, DG-SASRec reaches 0.047 Hit@10 versus 0.031 for SASRec, while DG-BERT4Rec reaches 0.046 versus 0.025 for BERT4Rec, with 66K trainable parameters (2--4% overhead). End-to-end retraining attains higher absolute accuracy but changes the backbone embeddings; the frozen plugin preserves zero backbone drift, uses about 40x fewer trainable parameters, and retains observable dimension-wise routing. The source code is available at https://github.com/jdding/DeltaGate.
