Recommendation models that utilize unique identities (IDs for short) to represent distinct
users and items have been state-of-the-art (SOTA) and dominated the recommender …

Abstract Graph Neural Networks have been applied in recommender systems to learn the
representation of users and items from a user-item graph. In the state-of-the-art, there are …

Micro-videos have recently gained immense popularity, sparking critical research in micro-
video recommendation with significant implications for the entertainment, advertising, and e …

The superior performance of modern deep networks usually comes with a costly training
procedure. This paper presents a new curriculum learning approach for the efficient training …

Recent advances in vision Transformers (ViTs) have come with a voracious appetite for
computing power, high-lighting the urgent need to develop efficient training methods for …

Learning user representations is a vital technique toward effective user modeling and
personalized recommender systems. Existing approaches often derive an individual set of …

Existing benchmark datasets for recommender systems (RS) either are created at a small
scale or involve very limited forms of user feedback. RS models evaluated on such datasets …

The recommender system which gets higher in practical use in applying the Apriori
algorithm in the early 2000s has revolutionized our daily life as it currently is widely used by …

Deep learning has brought significant breakthroughs in sequential recommendation (SR) for
capturing dynamic user interests. A series of recent research revealed that models with more …

Making accurate recommendations for cold-start users has been a longstanding and critical
challenge for recommender systems (RS). Cross-domain recommendations (CDR) offer a …