Recommender Systems are currently highly relevant for helping users deal with the
information overload they suffer from the large volume of data on the web, and automatically …

As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …

Heterogeneous information network (HIN) has been widely adopted in recommender
systems due to its excellence in modeling complex context information. Although existing …

Online communities such as Facebook and Twitter are enormously popular and have
become an essential part of the daily life of many of their users. Through these platforms …

The study focuses on sources of user-generated content (UGC) in social media: strong-tie
sources, weak-tie sources, and tourism-tie sources and their effects on tourist satisfaction …

Social recommender systems are expected to improve recommendation quality by
incorporating social information when there is little user-item interaction data. However …

Data in many real-world applications such as social networks, users shopping behaviors,
and inter-item relationships can be represented as graphs. Graph Neural Networks (GNNs) …

Learning disentangled representations for user intentions from multi-feedback (ie, positive
and negative feedback) can enhance the accuracy and explainability of recommendation …

Graph Neural Network (GNN) based social recommendation models improve the prediction
accuracy of user preference by leveraging GNN in exploiting preference similarity contained …

There exist complex interactions among a large number of latent factors behind the decision
making processes of different individuals, which drive the various user behavior patterns in …