Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs. However, conventional recommendation models solely conduct a one-time training-test fashion and can hardly adapt to evolving demands, considering user preference shifts and ever-increasing users and items in the real world. To tackle such challenges, the streaming recommendation is proposed and has attracted great attention recently. Among these, continual graph learning is widely regarded as a promising approach for the streaming recommendation by academia and industry. However, existing methods either rely on the historical data replay which is often not practical under increasingly strict data regulations, or can seldom solve the \textit{over-stability} issue. To overcome these difficulties, we propose a novel \textbf{D}ynamically \textbf{E}xpandable \textbf{G}raph \textbf{C}onvolution (DEGC) algorithm from a \textit{model isolation} perspective for the streaming recommendation which is orthogonal to previous methods. Based on the motivation of disentangling outdated short-term preferences from useful long-term preferences, we design a sequence of operations including graph convolution pruning, refining, and expanding to only preserve beneficial long-term preference-related parameters and extract fresh short-term preferences. Moreover, we model the temporal user preference, which is utilized as user embedding initialization, for better capturing the individual-level preference shifts. Extensive experiments on the three most representative GCN-based recommendation models and four industrial datasets demonstrate the effectiveness and robustness of our method.

翻译：个性化推荐系统已被广泛研究和部署，以减轻信息过载并满足用户的多样化需求。然而，传统推荐模型仅采用一次性训练-测试模式，难以适应现实世界中用户偏好变化以及用户和物品持续增长等演化需求。为应对这些挑战，流式推荐被提出并近年来受到广泛关注。其中，持续图学习被学术界和工业界普遍认为是实现流式推荐的有效途径。但现有方法要么依赖历史数据回放（这在日益严格的数据法规下往往不切实际），要么难以解决"过度稳定"问题。为解决这些难题，我们从模型隔离的角度出发，提出了一种全新的动态可扩展图卷积算法（Dynamically Expandable Graph Convolution, DEGC），该算法与以往方法正交。基于将过时短期偏好与有用长期偏好解耦的动机，我们设计了一系列操作，包括图卷积剪枝、细化和扩展，以仅保留有益的长期偏好相关参数并提取新鲜的短期偏好。此外，我们对用户时序偏好进行建模，并将其作为用户嵌入初始化，以更好地捕捉个体层面的偏好变化。在三种最具代表性的基于图卷积网络的推荐模型和四个工业数据集上的大量实验证明了我们方法的有效性和鲁棒性。