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 …

Knowledge graphs (KGs) have been widely used to improve recommendation accuracy. The
multi-hop paths on KGs also enable recommendation reasoning, which is considered a …

Recommender systems have emerged as indispensable tools for information filtering, and
the integration of knowledge graphs for auxiliary information is becoming an increasingly …

In this paper, we investigate a realistic but underexplored problem, called few-shot temporal
knowledge graph reasoning, that aims to predict future facts for newly emerging entities …

Abstract Knowledge Graph (KG) stores human knowledge facts in an intuitive graphical
structure but faces challenges such as incomplete construction or inability to handle new …

Traditional music recommender systems are mainly based on users' interactions, which limit
their performance. Particularly, various kinds of content information, such as metadata and …

As recommender systems become increasingly important in daily human decision-making,
users are demanding convincing explanations to understand why they get the specific …

As auxiliary collaborative signals, the entity connectivity and relation semanticity beneath
knowledge graph (KG) triples can alleviate the data sparsity and cold-start issues of …

Compared with only pursuing recommendation accuracy, the explainability of a
recommendation model has drawn more attention in recent years. Many graph-based …

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