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

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) 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 …

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 …

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 …

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

Collaborative Filtering (CF) is achieving a plateau of high popularity. Still, recommendation
success is challenged by the diversity of user preferences, structural sparsity of user-item …

In an era dominated by information overload, effective recommender systems are essential
for managing the deluge of data across digital platforms. Multi-stage cascade ranking …

Recommendation datasets are prone to selection biases due to self-selection behavior of
users and item selection process of systems. This makes explicitly combating selection …

Sequential recommendation as an emerging topic has attracted increasing attention due to
its important practical significance. Models based on deep learning and attention …