English(EN) Continual Graph Memory for Adaptive Recommendation under Intent Drift

新框架使推荐系统能够适应用户意图漂移

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员推出了一种名为CGM-Rec的新型框架，旨在通过适应不断变化的用户意图来增强推荐系统。与将知识图谱视为静态的传统系统不同，CGM-Rec将图谱视为可写内存。它采用语义图记忆来保持稳定的知识，并采用情景学习记忆来记录近期结果和失败案例。这种方法允许通过内存写入进行适应，而无需更改模型参数，在各种推荐场景中显示出优于现有基于神经网络和LLM的基线方法的显著改进。 AI

影响该框架通过更好地处理用户意图的变化，有望提高推荐系统的适应性和准确性。

排序理由介绍推荐系统新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Hao Nguyen Ngoc, Tung Nguyen, Nguyen Thi Hanh, Hoang Thai Dinh, Nguyen Xuan Tung · 2026-09-07 04:00

面向意图漂移下的自适应推荐的持续图记忆

arXiv:2609.04651v1 Announce Type: new Abstract: This paper studies adaptive recommendation under intent drift, where feedback from each recommendation outcome can reveal whether the relational evidence used for ranking is useful, missing, or misleading. While Knowledge Graphs (KG…

本文如何被排名

报道来源 [1]

面向意图漂移下的自适应推荐的持续图记忆

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