NMKFR：一种用于时间感知冷启动推荐的鲁棒框架
NMKFR: A Robust Framework for Time-Aware Cold-Start Recommendation
AI总结：
针对新项目冷启动推荐难题，提出 NMKFR 框架，结合 Titans 语义编码器与时间卡尔曼跟踪，经亚马逊视频游戏等数据集实验，其性能最优且不确定性相关行为受限。
AI中文摘要：
当新项目的早期交互稀疏且推荐环境随时间不断变化时，项目冷启动推荐颇具难度。静态内容、早期反馈和时间状态证据均有用，但它们在项目生命周期中的可靠性各不相同。本研究提出了一种框架——神经记忆卡尔曼融合推荐器（Neural Memory Kalman Fusion Recommender，NMKFR），它结合了基于 Titans 的语义编码器与时间感知卡尔曼状态跟踪。语义分支从文本中提取增强记忆的项目观测值，而时间分支则在不规则交互间隔下估计隐状态。NMKFR 还利用后验协方差作为不确定性信号，来校准语义记忆检索与自适应静态-时间融合。在亚马逊视频游戏数据集和 MovieLens-32M 数据集上的实验，通过采样候选排序在时间感知和项目冷启动协议下评估了 NMKFR。在报告的比较、 ablation（ ablation 即消融实验）、诊断和鲁棒性分析中，NMKFR 取得了最强的保留结果，并表现出与不确定性相关的受限内部行为。这些发现为所评估的离线设置下，后验协方差引导的语义-时间融合提供了实证依据。
英文摘要：
Item cold-start recommendation is difficult when new items have sparse early interactions and appear in recommendation environments that keep changing over time. Static content, early feedback, and temporal-state evidence are all useful, but their reliability varies across the item lifecycle. This work proposes a framework--Neural Memory Kalman Fusion Recommender (NMKFR), which combines a Titans-based semantic encoder with time-aware Kalman state tracking. The semantic branch extracts memory-enhanced item observations from text, while the temporal branch estimates latent states under irregular interaction intervals. The NMKFR further uses posterior covariance as an uncertainty signal to calibrate semantic memory retrieval and adaptive static-temporal fusion. Experiments on Amazon Video Games and MovieLens-32M evaluate NMKFR under time-aware and item cold-start protocols using sampled candidate ranking. Across the reported comparisons, ablations, diagnostics, and robustness analyses, NMKFR achieves the strongest retained results and exhibits bounded uncertainty-related internal behavior. These findings provide empirical evidence for posterior-covariance-guided semantic-temporal fusion under the evaluated offline settings.
