解决新物品推荐中因数据稀疏和时间变化带来的难题。
NMKFR: A Robust Framework for Time-Aware Cold-Start Recommendation
- 用语义编码与卡尔曼滤波融合追踪物品随时间演变的状态。
- 在亚马逊游戏和MovieLens-32M上实现最佳排名性能。
- 适合关注冷启动推荐与动态环境建模的研究者。
新物品在早期交互数据稀疏且推荐环境持续变化时，冷启动推荐尤为困难。静态内容、早期反馈和时间状态证据虽有用，但其可靠性随物品生命周期变化。本文提出神经记忆卡尔曼融合推荐框架（NMKFR），结合基于Titans的语义编码器与时间感知卡尔曼状态追踪。语义分支从文本中提取增强记忆的物品表征，时间分支在不规则交互间隔下估计潜在状态。NMKFR进一步利用后验协方差作为不确定性信号，校准语义记忆检索并实现自适应的静态-时间融合。在Amazon Video Games和MovieLens-32M上的实验表明，在时间感知与冷启动评估协议下，使用采样候选排名，NMKFR在各项对比、消融、诊断及鲁棒性分析中均表现最优，内部行为具有限定的不确定性特征。这些结果为后验协方差引导的语义-时间融合提供了实证支持。
原文摘要 · Abstract (English)
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.
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