To address the long-standing data sparsity problem in recommender systems (RSs), cross-
domain recommendation (CDR) has been proposed to leverage the relatively richer …

Traditional recommendation systems are faced with two long-standing obstacles, namely
data sparsity and cold-start problems, which promote the emergence and development of …

Cross Domain Recommendation (CDR) has been popularly studied to alleviate the cold-
start and data sparsity problem commonly existed in recommender systems. CDR models …

Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-
world tasks on graph data, consisting of node features and the adjacent information between …

Recommender systems have been widely deployed in various real-world applications to
help users identify content of interest from massive amounts of information. Traditional …

The emergence of ubiquitous computing and different disruptive technologies caused
magnificent development in information and communication technology. Likewise …

Logistic Regression (LR) is the most widely used machine learning model in industry for its
efficiency, robustness, and interpretability. Due to the problem of data isolation and the …

Vertical federated learning (VFL) is a promising category of federated learning for the
scenario where data is vertically partitioned and distributed among parties. VFL enriches the …

Point-of-Interest (POI) recommendation has been extensively studied and successfully
applied in industry recently. However, most existing approaches build centralized models on …

Federated learning has recently been applied to recommendation systems to protect user
privacy. In federated learning settings, recommendation systems can train recommendation …