Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …

In recent times, the recommender systems (RSs) have considerable importance in
academia, commercial activities, and industry. They are widely used in various domains …

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 …

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 …

Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is
attributed to their capability on learning good user and item embeddings by exploiting the …

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

Incorporating knowledge graph (KG) into recommender system is promising in improving the
recommendation accuracy and explainability. However, existing methods largely assume …

Reorganizing implicit feedback of users as a user-item interaction graph facilitates the
applications of graph convolutional networks (GCNs) in recommendation tasks. In the …

Online streaming feature selection (OSFS) has attracted extensive attention during the past
decades. Current approaches commonly assume that the feature space of fixed data …