Large language models (LLMs) are a special class of pretrained language models (PLMs)
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …

Recommendation systems have witnessed significant advancements and have been widely
used over the past decades. However, most traditional recommendation methods are task …

Transformer-based sequential recommendation (SR) has been booming in recent years,
with the self-attention mechanism as its key component. Self-attention has been widely …

Contrastive learning (CL) benefits the training of sequential recommendation models with
informative self-supervision signals. Existing solutions apply general sequential data …

Although Transformer has achieved great success in natural language process and
computer vision, it has difficulty generalizing to medium and large-scale graph data for two …

The sequential recommendation task based on the multi-interest framework aims to model
multiple interests of users from different aspects to predict their future interactions. However …

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 …

Acquiring high-quality representations for both users and items is essential, facilitating a
wide range of recommendation scenarios. Utilizing graph neural networks for knowledge …

Recommendation system (RS) plays significant roles in matching users information needs
for Internet applications, and it usually utilizes the vanilla neural network as the backbone to …

Sequential recommender systems (SRS) are designed to predict users' future behaviors
based on their historical interaction data. Recent research has increasingly utilized …