Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …

Recommender systems play a crucial role in tackling the challenge of information overload
by delivering personalized recommendations based on individual user preferences. Deep …

Graph Neural Network (GNN) based recommender systems have been attracting more and
more attention in recent years due to their excellent performance in accuracy. Representing …

Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …

With the widespread application of online advertising systems, click-through rate (CTR)
prediction has received more and more attention and research. The most prominent features …

The rise of Large Language Models (LLMs) has sparked interest in their application to
sequential recommendation tasks as they can provide supportive item information. However …

Based on historical behaviors, sequential recommendation endeavors to predict what a user
prefers next. The recent efforts are mainly devoted to modeling the user's interests evolution …

Session-based recommendation is devoted to characterizing preferences of anonymous
users based on short sessions. Existing methods mostly focus on mining limited item co …

Generative models are a class of AI models capable of creating new instances of data by
learning and sampling from their statistical distributions. In recent years, these models have …

Contemporary attention-based sequential recommendations often encounter the
oversmoothing problem, which generates indistinguishable representations. Although …