Recommender systems have become an important instrument to connect people to
information. Sparse, complex, and rapidly growing data presents new challenges to …

Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-
Encoders (VAEs) are widely utilized to model the generative process of user interactions …

With the rapid growth of information, recommender systems have become integral for
providing personalized suggestions and overcoming information overload. However, their …

Collaborative filtering (CF) plays a critical role in the development of recommender systems.
Most CF methods utilize an encoder to embed users and items into the same representation …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Collaborative filtering (CF) is a widely studied research topic in recommender systems. The
learning of a CF model generally depends on three major components, namely interaction …

By treating users' interactions as a user-item graph, graph learning models have been
widely deployed in Collaborative Filtering~(CF) based recommendation. Recently …

The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …

In recent years, there are a large number of recommendation algorithms proposed in the
literature, from traditional collaborative filtering to deep learning algorithms. However, the …

Aiming at exploiting the rich information in user behaviour sequences, sequential
recommendation has been widely adopted in real-world recommender systems. However …