The comprehensive evaluation of the performance of a recommender system is a complex
endeavor: many facets need to be considered in configuring an adequate and effective …

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

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

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of
recommendation, since its ability to extract self-supervised signals from the raw data is well …

Recently, graph collaborative filtering methods have been proposed as an effective
recommendation approach, which can capture users' preference over items by modeling the …

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 …

Recommender systems are the cornerstone of today's information dissemination, yet a
disconnect between offline metrics and online performance greatly hinders their …

Learning informative representations of users and items from the interaction data is of crucial
importance to collaborative filtering (CF). Present embedding functions exploit user-item …

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative
filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well …

An autoencoder is a specific type of a neural network, which is mainly designed to encode
the input into a compressed and meaningful representation and then decode it back such …