The problem of data sparsity has long been a challenge in recommendation systems, and
previous studies have attempted to address this issue by incorporating side information …

Large language models (LLMs) have demonstrated impressive reasoning abilities in
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Model editing techniques modify a minor proportion of knowledge in Large Language
Models (LLMs) at a relatively low cost, which have demonstrated notable success. Existing …

Multivariate time series long-term prediction, which aims to predict the change of data in a
long time, can provide references for decision-making. Although transformer-based models …

Multi-view clustering (MVC), which effectively fuses information from multiple views for better
performance, has received increasing attention. Most existing MVC methods assume that …

Multimodal knowledge graphs (MKGs), which intuitively organize information in various
modalities, can benefit multiple practical downstream tasks, such as recommendation …

Deep clustering with attribute-missing graphs, where only a subset of nodes possesses
complete attributes while those of others are missing, is an important yet challenging topic in …

Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …

The spatio-temporal wind speed prediction technology provides the key technical support for
the energy management and space allocation of the wind farm. To obtain an accurate spatio …