Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

In the field of sequential recommendation, deep learning--(DL) based methods have
received a lot of attention in the past few years and surpassed traditional models such as …

Neural graph collaborative filtering has received great recent attention due to its power of
encoding the high-order neighborhood via the backbone graph neural networks. However …

Abstract Knowledge graphs (KGs) have proven to be effective for high-quality
recommendation, where the connectivities between users and items provide rich and …

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

Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual
improvement among tasks considering their shared knowledge. It is an important topic in …

As an emerging paradigm, session-based recommendation is aimed at recommending the
next item based on a set of anonymous sessions. Effectively representing a session that is …

Sequential Recommendation (SR) captures users' dynamic preferences by modeling how
users transit among items. However, SR models that utilize only single type of behavior …