Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to
capture structural, temporal, and contextual relationships in dynamic graphs simultaneously …

Continual learning on graph data has recently attracted paramount attention for its aim to
resolve the catastrophic forgetting problem on existing tasks while adapting the sequentially …

Recommender systems (RS) are effective tools for mitigating information overload and have
seen extensive applications across various domains. However, the single focus on utility …

Large Language Models (LLMs) have demonstrated remarkable capabilities and have been
extensively deployed across various domains, including recommender systems. Prior …

Conventional recommendation methods have achieved notable advancements by
harnessing collaborative or sequential information from user behavior. Recently, large …

Personalized recommender systems have found widespread applications for effective
information filtering. Conventional models engage in knowledge mining within the static …

The classic problem of node importance estimation has been conventionally studied with
homogeneous network topology analysis. To deal with practical network heterogeneity, a …

Maximizing the user-item engagement based on vectorized embeddings is a standard
procedure of recent recommender models. Despite the superior performance for item …

In the realm of personalized recommender systems, the challenge of adapting to evolving
user preferences and the continuous influx of new users and items is paramount …

With the continuous increase of users and items, conventional recommender systems
trained on static datasets can hardly adapt to changing environments. The high-throughput …