  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 Based on the motivation of disentangling outdated short-term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.
