Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing
users and items into latent representation space, with their correlative patterns from …

A well-informed recommendation framework could not only help users identify their
interested items, but also benefit the revenue of various online platforms (eg, e-commerce …

Current sequential recommender systems are proposed to tackle the dynamic user
preference learning with various neural techniques, such as Transformer and Graph Neural …

Accurate user and item embedding learning is crucial for modern recommender systems.
However, most existing recommendation techniques have thus far focused on modeling …

Learning dynamic user preference has become an increasingly important component for
many online platforms (eg, video-sharing sites, e-commerce systems) to make sequential …

Recent years have witnessed the remarkable success of recommendation systems (RSs) in
alleviating the information overload problem. As a new paradigm of RSs, session-based …

Session-based recommendation aims to predict items that an anonymous user would like to
purchase based on her short behavior sequence. The current approaches towards session …

In recent years, researchers attempt to utilize online social information to alleviate data
sparsity for collaborative filtering, based on the rationale that social networks offers the …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Modeling time-evolving preferences of users with their sequential item interactions, has
attracted increasing attention in many online applications. Hence, sequential recommender …