Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Hypergraphs (ie, sets of hyperedges) naturally represent group relations (eg, researchers co-
authoring a paper and ingredients used together in a recipe), each of which corresponds to …

In this work, we introduce a hypergraph representation learning framework called
Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a …

Food recommendation systems have become pivotal in offering personalized suggestions,
enabling users to discover recipes in line with their tastes. However, despite the existence of …

Graph anomaly detection aims to identify unusual patterns or structures in graph-structured
data. Most existing research focuses on anomalous nodes in ordinary graphs with pairwise …

In the dynamic field of information retrieval, the adoption of graph-based approaches has
become a notable research trend. Fueled by the growing research on Knowledge Graphs …

The rising popularity of conversational agents for accessing information stems from their
natural language dialogue-based interaction, facilitating usability for a broad spectrum of …

The interaction with information access systems through conversational interfaces is
becoming increasingly popular. Among the systems that benefit the most from this kind of …

We conducted our literature review by in-depth reading, interpreting, and categorizing
articles addressing the problem of generating a low-dimensional representation of a …

The difference between a search and recommendation comes from the presence of a query.
A search includes query, while a recommendation does not include one. A model that …