Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …

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 …

Large Language Models (LLMs) have gained the ability to assimilate human knowledge and
facilitate natural language interactions with both humans and other LLMs. However, despite …

The representation learning on textual graph is to generate low-dimensional embeddings for
the nodes based on the individual textual features and the neighbourhood information …

Session-based recommendation (SBR) aims to predict the user's next action based on short
and dynamic sessions. Recently, there has been an increasing interest in utilizing various …

Recently, large language models (LLMs) have demonstrated superior capabilities in
understanding and zero-shot learning on textual data, promising significant advances for …

Text-attributed graphs (TAGs) are prevalent in various real-world scenarios, where each
node is associated with a text description. The cornerstone of representation learning on …

Sequential recommendation (SR) aims to model users' dynamic preferences from a series of
interactions. A pivotal challenge in user modeling for SR lies in the inherent variability of …

Edges in many real-world social/information networks are associated with rich text
information (eg, user-user communications or user-product reviews). However, mainstream …

Representation learning on networks aims to derive a meaningful vector representation for
each node, thereby facilitating downstream tasks such as link prediction, node classification …