CF-KAN：基于柯尔莫哥洛夫-阿诺德网络的协同过滤方法，用于缓解推荐系统中的灾难性遗忘问题
CF-KAN: Kolmogorov-Arnold Network-based Collaborative Filtering to Mitigate Catastrophic Forgetting in Recommender Systems
AI总结：
针对推荐系统中基于MLP的协同过滤方法易出现灾难性遗忘的问题，提出基于KAN自编码器的CF-KAN方法，在准确率、抗遗忘能力和可解释性上均优于现有最优方法。
摘要：
协同过滤（CF）仍是推荐系统中的核心技术，它利用用户-物品交互信息提供个性化推荐。与此同时，诸多CF技术已发展为基于多层感知机（MLP）的复杂模型架构。然而，MLP常受灾难性遗忘问题困扰，在学习新信息时会丢失先前习得的知识，在需要持续学习的动态环境中尤为明显。为解决这一问题，我们提出CF-KAN——一种利用柯尔莫哥洛夫-阿诺德网络（KAN）的新型CF方法。KAN通过在边层面学习非线性函数，对灾难性遗忘问题的鲁棒性优于MLP。CF-KAN基于KAN自编码器构建，旨在有效捕捉稀疏用户-物品交互的复杂特征，并保留先前数据样本的信息。尽管结构简单，我们的大量实验表明：1）CF-KAN在推荐准确率上优于现有最优方法；2）CF-KAN对灾难性遗忘具有韧性，在静态和动态推荐场景中均表现出有效性；3）CF-KAN的边层面可解释性有助于提升推荐的可解释性。
英文摘要：
Collaborative filtering (CF) remains essential in recommender systems, leveraging user--item interactions to provide personalized recommendations. Meanwhile, a number of CF techniques have evolved into sophisticated model architectures based on multi-layer perceptrons (MLPs). However, MLPs often suffer from catastrophic forgetting, and thus lose previously acquired knowledge when new information is learned, particularly in dynamic environments requiring continual learning. To tackle this problem, we propose CF-KAN, a new CF method utilizing Kolmogorov-Arnold networks (KANs). By learning nonlinear functions on the edge level, KANs are more robust to the catastrophic forgetting problem than MLPs. Built upon a KAN-based autoencoder, CF-KAN is designed in the sense of effectively capturing the intricacies of sparse user--item interactions and retaining information from previous data instances. Despite its simplicity, our extensive experiments demonstrate 1) CF-KAN's superiority over state-of-the-art methods in recommendation accuracy, 2) CF-KAN's resilience to catastrophic forgetting, underscoring its effectiveness in both static and dynamic recommendation scenarios, and 3) CF-KAN's edge-level interpretation facilitating the explainability of recommendations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
