用柯尔莫哥洛夫网络缓解推荐系统遗忘问题
CF-KAN: Kolmogorov-Arnold Network-based Collaborative Filtering to Mitigate Catastrophic Forgetting in Recommender Systems
- 基于柯尔莫哥洛夫网络构建推荐模型，增强对历史数据的保留能力
- 在静态与动态场景下均优于现有方法，显著减少灾难性遗忘
- 可解释性强，适合需要可信推荐的工业场景
协同过滤（CF）在推荐系统中仍具核心地位，依赖用户-物品交互提供个性化推荐。尽管许多CF方法已演变为基于多层感知机（MLPs）的复杂架构，但MLPs在持续学习过程中常出现灾难性遗忘，导致新信息学习时丢失旧知识，尤其在动态环境中表现不佳。为此，本文提出基于柯尔莫哥洛夫-阿诺德网络（KANs）的CF-KAN方法。通过在边级别学习非线性函数，KANs比MLPs更抗遗忘。基于KAN的自编码器设计，使CF-KAN能有效捕捉稀疏的用户-物品交互，并保留先前数据实例的信息。大量实验表明：1）CF-KAN在推荐准确率上优于当前最优方法；2）对灾难性遗忘具有强鲁棒性，适用于静态与动态推荐场景；3）边级可解释性提升了推荐结果的透明度。
原文摘要 · Abstract (English)
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.
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