The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …

Recommender systems play a significant role in information filtering and have been utilized
in different scenarios, such as e-commerce and social media. With the prosperity of deep …

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 …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

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

Link prediction aims to identify potential missing triples in knowledge graphs. To get better
results, some recent studies have introduced multimodal information to link prediction …

Cross-Domain Recommendation (CDR) is a widely used approach for leveraging
information from domains with rich data to assist domains with insufficient data. A key …

Historical user-item interaction datasets are essential in training modern recommender
systems for predicting user preferences. However, the arbitrary user behaviors in most …

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

Click-through prediction (CTR) models transform features into latent vectors and enumerate
possible feature interactions to improve performance based on the input feature set …