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

Machine learning has been heavily researched and widely used in many disciplines.
However, achieving high accuracy requires a large amount of data that is sometimes …

Representation learning on user-item graph for recommendation has evolved from using
single ID or interaction history to exploiting higher-order neighbors. This leads to the …

Learning vector representations (aka. embeddings) of users and items lies at the core of
modern recommender systems. Ranging from early matrix factorization to recently emerged …

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 …

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

Recommender system plays a vital role in various online services. However, its insulated
nature of training and deploying separately within a specific closed domain limits its access …

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

Knowledge graph (KG) plays an increasingly important role in recommender systems. A
recent technical trend is to develop end-to-end models founded on graph neural networks …

Personalized recommendation plays a central role in many online content sharing platforms.
To provide quality micro-video recommendation service, it is of crucial importance to …