With the rapid development of online multimedia services, especially in e-commerce
platforms, there is a pressing need for personalised recommender systems that can …

Recommender systems remain an essential topic due to its wide application in various
domains and the business potential behind them. With the rise of deep learning, common …

Most recommender systems research focuses on binary historical user-item interaction
encodings to predict future interactions. User features, item features, and interaction …

In recent years, the rapid growth of online multimedia services, such as e-commerce
platforms, has necessitated the development of personalised recommendation approaches …

Real-world recommendation scenarios usually need to handle diverse user-item interaction
behaviours, including page views, adding items into carts, and purchasing activities. The …

The distributionally robust optimization (DRO)-based graph neural network methods
improve recommendation systems' out-of-distribution (OOD) generalization by optimizing the …

Generative models have shown great promise in collaborative filtering by capturing the
underlying distribution of user interests and preferences. However, existing approaches …

While traditional recommendation techniques have made significant strides in the past
decades, they still suffer from limited generalization performance caused by factors like …

Recovering user preferences from user-item interaction matrices is a key challenge in
recommender systems. While diffusion models can sample and reconstruct preferences from …