发表机构
Fudan University; Huawei Technologies（复旦大学; 华为技术）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究零观测用户重新激活问题，提出轻量级输出层插件DeltaGate，在三个亚马逊数据集实验，其能在保持主干冻结下，通过间隙和个性化表示调节路由维度，相比其他方法提升了Hit@10指标，且参数少、保留主干漂移。
AI 中文摘要
顺序推荐（SR）模型捕捉持续观察到的行为，但回访用户可能数月或数年无交互。我们将此设置定义为零观测重新激活：用户有预间隙历史，而平台在宏观间隙Δt期间未观察到行为信号。在三个亚马逊数据集上的按时间对齐的间隙合成协议下，命中@10在评估的间隙桶中单调下降，超过一年达到最低水平。我们提出了DeltaGate，一种轻量级输出层插件，它保持主干冻结，并在个性化历史和学习到的零初始化全局先验之间路由每个表示维度。门由Δt和个性化表示共同调节。在控制诊断中，我们固定个性化表示并改变Δt以隔离训练的门对间隙输入的响应。在>365d视频游戏桶中，DG - SASRec的命中@10达到0.047，而SASRec为0.031，DG - BERT4Rec达到0.046，而BERT4Rec为0.025，有66K可训练参数（2 - 4%开销）。端到端重新训练获得更高的绝对精度，但改变了主干嵌入；冻结插件保留零主干漂移，使用约40倍少的可训练参数，并保留可观察的维度路由。源代码可在该https URL获取。
英文摘要
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.
CommentsAccepted at the 20th ACM Conference on Recommender Systems (RecSys 2026)
