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 …

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

Network operation and maintenance rely heavily on network traffic monitoring. Due to the
measurement overhead reduction, lack of measurement infrastructure, and unexpected …

With the digitization of modern cities, large data volumes and powerful computational
resources facilitate the rapid update of intelligent models deployed in smart cities. Continual …

Recommender systems are incremental in nature. Recent progresses in incremental
recommendation rely on capturing the temporal dynamics of users/items from temporal …

Collaborative filtering (CF) methods based on graph convolutional network (GCN) and
autoencoder (AE) achieve outstanding performance. But the GCN-based CF methods suffer …

Auxiliary information, such as reviews, have been widely adopted to improve collaborative
filtering (CF) algorithms, eg, to boost the accuracy and provide explanations. However, most …

Recently, continual graph learning has been increasingly adopted for diverse graph-
structured data processing tasks in non-stationary environments. Despite its promising …

Temporal recommendation methods can achieve superior accuracy due to updating
user/item embeddings continuously once obtaining new interactions. However, the …

Graph Signal Processing (GSP) has proven to be a highly effective and efficient tool for
predicting user future interactions in recommender systems. However, current GSP methods …