In this technical survey, the latest advancements in the field of recommender systems are
comprehensively summarized. The objective of this study is to provide an overview of the …

As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …

By providing personalized suggestions to users, recommender systems have become
essential to numerous online platforms. Collaborative filtering, particularly graph-based …

The graph neural network-based collaborative filtering (CF) models user-item interactions as
a bipartite graph and performs iterative aggregation to enhance performance. Unfortunately …

Historical Quality-of-Service (QoS) data describing existing user-service invocations are vital
to understanding user behaviors and cloud service conditions. Collaborative Filtering (CF) …

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

Recommender Systems (RSs) are used to provide users with personalized item
recommendations and help them overcome the problem of information overload. Currently …

Collaborative Filtering (CF) plays a crucial role in modern recommender systems, leveraging
historical user-item interactions to provide personalized suggestions. However, CF-based …

Graph neural network (GNN) based algorithms have achieved superior performance in
recommendation tasks due to their advanced capability of exploiting high-order connectivity …

Graph Neural Network (GNN)-based models have become the mainstream approach for
recommender systems. Despite the effectiveness, they are still suffering from the cold-start …