基于社交网络关系，实时检测异常用户并推荐个性化内容
Anomaly Detection in Networked Bandits
- 利用网络结构分析用户偏好与特征残差
- 实现推荐与异常检测同步，理论证明误差有界
- 适用于社交推荐、安全监控等场景
社交网络中节点间的连接常反映其依赖关系与信息共享行为。然而，行为显著偏离多数节点的异常节点可能带来严重后果。因此，亟需设计高效在线学习算法，在稳健学习用户偏好同时实现异常检测。本文提出一种新型贝叶斯带子算法，通过网络知识刻画用户偏好及特征信息残差，结合学习与分析这些信号，为每个用户生成个性化推荐策略并同步检测异常。我们严格证明了该算法的后悔上界，并在合成数据与真实世界数据集上，与多种前沿协同上下文带子算法进行了实验对比。
原文摘要 · Abstract (English)
The nodes' interconnections on a social network often reflect their dependencies and information-sharing behaviors. Nevertheless, abnormal nodes, which significantly deviate from most of the network concerning patterns or behaviors, can lead to grave consequences. Therefore, it is imperative to design efficient online learning algorithms that robustly learn users' preferences while simultaneously detecting anomalies. We introduce a novel bandit algorithm to address this problem. Through network knowledge, the method characterizes the users' preferences and residuals of feature information. By learning and analyzing these preferences and residuals, it develops a personalized recommendation strategy for each user and simultaneously detects anomalies. We rigorously prove an upper bound on the regret of the proposed algorithm and experimentally compare it with several state-of-the-art collaborative contextual bandit algorithms on both synthetic and real-world datasets.
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