发表机构
School of Mathematics and Computing (Computational Science and Engineering), Yonsei University（延世大学数学与计算学院（计算科学与工程））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MATE提出基于时间证据的自适应用户记忆框架，通过长期与短期记忆动态融合，在LLM推荐中提升序列推荐性能，实验显示NDCG@10显著优于基线。
AI 中文摘要
大语言模型（LLM）增强的推荐系统利用丰富的物品语义来支持个性化推荐。然而，仅凭语义表示并不能确定哪些历史行为反映了持久的偏好，哪些主要指示了近期兴趣，这留下了用户理解的一个重要方面尚未解决。LLM推理的最新进展表明，新获得的信息可用于在推理过程中优化内部状态，从而改进后续预测。受此原理启发，我们提出了MATE（带时间证据的记忆自适应），一个用于LLM增强的序列推荐的自适应用户建模框架。MATE首先从两个时间角度评估每个新观察到的交互：它是否被历史行为反复支持，以及它是否与近期交互一致。由此产生的时间证据控制两个用户特定记忆的更新，其中长期记忆保守地保留持久偏好，而短期记忆快速适应近期兴趣。对于每次推荐，一个近期上下文表示动态决定这两个记忆对当前用户表示的贡献程度。在离线训练期间，下一项预测与时间监督联合优化，而在在线适应期间，共享模型保持固定，仅从新观察到的交互中更新两个用户记忆。在MovieLens-10M、Amazon Luxury Beauty和KuaiRec上的实验表明，MATE在平均NDCG@10上比最强外部基线提高了7.0%至13.2%。进一步的分析支持其适应近期兴趣同时保留关于反复出现的早期偏好的有用信息的能力。
英文摘要
Large language model (LLM)-enhanced recommender systems leverage rich item semantics to support personalized recommendation. However, semantic representations alone do not determine which historical behaviors reflect persistent preferences and which mainly indicate recent interests, leaving an important aspect of user understanding unresolved. Recent advances in LLM inference show that newly available information can be used to refine the internal state during inference, thereby improving subsequent predictions. Inspired by this principle, we propose MATE (Memory Adaptation with Temporal Evidence), an adaptive user modeling framework for LLM-enhanced sequential recommendation. MATE first evaluates each newly observed interaction from two temporal perspectives: whether it is repeatedly supported by historical behaviors and whether it is consistent with recent interactions. The resulting temporal evidence controls the updates of two user-specific memories, where the long-term memory conservatively preserves persistent preferences while the short-term memory rapidly adapts to recent interests. For each recommendation, a recent-context representation dynamically determines how strongly the two memories contribute to the current user representation. During offline training, next-item prediction is jointly optimized with temporal supervision, while during online adaptation, the shared model remains fixed and only the two user memories are updated from newly observed interactions. Experiments on MovieLens-10M, Amazon Luxury Beauty, and KuaiRec show that MATE improves mean NDCG@10 over the strongest external baseline by 7.0--13.2%. Further analyses support its ability to adapt to recent interests while retaining useful information about recurring earlier preferences.
