The provision of privacy-preserving recommendations for geological tourist attractions is an
important research area. The historical check-in data collected from location-based social …

Recommender systems become essential with the presence of the internet and social
media. The perceived benefits of the recommender system can make it easier for users to …

The problem of community-level information pathway prediction (CLIPP) aims at predicting
the transmission trajectory of content across online communities. A successful solution to …

Collaborative filtering is one of the most fundamental topics for recommender systems.
Various methods have been proposed for collaborative filtering, ranging from matrix …

Collaborative filtering methods based on graph neural networks (GNNs) have witnessed
significant success in recommender systems (RS), capitalizing on their ability to capture …

Collaborative filtering (CF) is an important research direction in recommender systems that
aims to make recommendations given the information on user-item interactions. Graph CF …

Collaborative filtering (CF) is a widely employed technique that predicts user preferences
based on past interactions. Negative sampling plays a vital role in training CF-based models …

To date, graph collaborative filtering (CF) strategies have been shown to outperform pure CF
models in generating accurate recommendations. Nevertheless, recent works have raised …

The success of graph neural network-based models (GNNs) has significantly advanced
recommender systems by effectively modeling users and items as a bipartite, undirected …