Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …

Knowledge Graphs (KGs) have been utilized as useful side information to improve
recommendation quality. In those recommender systems, knowledge graph information …

To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go
beyond modeling user-item interactions and take side information into account. Traditional …

To alleviate sparsity and cold start problem of collaborative filtering based recommender
systems, researchers and engineers usually collect attributes of users and items, and design …

Knowledge graphs capture structured information and relations between a set of entities or
items. As such knowledge graphs represent an attractive source of information that could …

Incorporating knowledge graph into recommender systems has attracted increasing
attention in recent years. By exploring the interlinks within a knowledge graph, the …

Recent years have witnessed the fast development of the emerging topic of Graph Learning
based Recommender Systems (GLRS). GLRS employ advanced graph learning …

To address the sparsity and cold start problem of collaborative filtering, researchers usually
make use of side information, such as social networks or item attributes, to improve …