学习遗忘：用于减轻购买后冗余的饱腹感感知长序列变换器
Learning to Forget: Satiation-Aware Long-Sequence Transducers for Mitigating Post-Purchase Redundancy
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中文总结 AI 辅助
针对电商场景中购买行为常意味兴趣终止，现有模型存在行动-意图不对称致购买后冗余的问题，提出饱腹感感知机制SAM，含双路径交叉注意力等三个关键组件，实验表明其显著降低购买后重复率超60%。
中文摘要 AI 辅助
顺序推荐模型主要将用户交互解释为偏好积累的积极信号。然而，在电子商务场景中，购买行为通常意味着特定意图的终止（“兴趣退出”）而非延续。现有模型忽视了这一区别，存在行动-意图不对称问题，导致严重的购买后冗余。本文提出了饱腹感感知机制（SAM），这是一个端到端框架，旨在明确模拟用户兴趣的生命周期。SAM包含三个关键组件：双路径交叉注意力架构、自适应饱腹感门控单元和自监督的下次购买时间辅助任务。在工业数据集上的大量离线实验和在线A/B测试表明，SAM显著降低了60%以上的购买后重复率。
英文摘要
Sequential recommendation models predominantly interpret user interactions as positive signals for preference accumulation. However, in e-commerce scenarios, a purchase action often signifies the termination of a specific intent ("Interest Exit") rather than its continuation. Existing models overlook this distinction, suffering from Action-Intent Asymmetry, which leads to severe post-purchase redundancy. In this paper, we propose the Satiation-Aware Mechanism (SAM), an end-to-end framework designed to explicitly model the lifecycle of user interests. SAM incorporates three key components: (1) A Dual-path Cross-Attention architecture that retroactively suppresses historical clicks associated with a fulfilled intent while simultaneously retrieving personalized replenishment rhythms from long-term purchase history; (2) An Adaptive Satiation Gating Unit (ASGU) that generates a time-sensitive soft mask to inhibit satisfied interests immediately after purchase and gradually "re-awaken" them as the predicted repurchase cycle approaches; and (3) A self-supervised Time-to-Next-Purchase (TTNP) auxiliary task to learn latent product lifecycles without manual annotation. Extensive offline experiments on industrial datasets and online A/B testing demonstrate that SAM significantly reduces the Post-Purchase Repeat Rate (PPRR) by over 60%.
