Sequential recommendation (SRS) has become the technical foundation in many
applications recently, which aims to recommend the next item based on the user's historical …

Transformer models have achieved remarkable success in sequential recommender
systems (SRSs). However, computing the attention matrix in traditional dot-product attention …

State-of-the-art sequential recommendation relies heavily on self-attention-based
recommender models. Yet such models are computationally expensive and often too slow …

Multi-task Recommender Systems (MTRSs) has become increasingly prevalent in a variety
of real-world applications due to their exceptional training efficiency and recommendation …

In the recent advancements of recommendation systems, the integration of deep learning
models has significantly enhanced prediction accuracy and user experience. This paper …

Multi-Domain Recommendation (MDR) has gained significant attention in recent years,
which leverages data from multiple domains to enhance their performance concurrently …

In this work, we present TriMLP as a foundational MLP-like architecture for the sequential
recommendation, simultaneously achieving computational efficiency and promising …

With the rapid evolution of transformer architectures, researchers are exploring their
application in sequential recommender systems (SRSs) and presenting promising …

Deep Recommender Systems (DRS) are increasingly dependent on a large number of
feature fields for more precise recommendations. Effective feature selection methods are …

Self-attention models have achieved the state-of-the-art performance in sequential
recommender systems by capturing the sequential dependencies among user–item …