Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …

Recommender systems, in real-world circumstances, tend to limit user exposure to certain
topics and to overexpose them to others to maximize performance. However, repeated …

Transfer learning-based recommendation mitigates the sparsity of user-item interactions by
introducing auxiliary domains. Social influence extracted from direct connections between …

Item recommendation and trust prediction are desired by users on social network platforms
since they can help users find their favourite items or friends faster. Existing methods usually …

Environmental e-commerce is a sustainability-oriented e-commerce model. To address the
problem of data sparsity and the lack of diversity in traditional e-commerce recommendation …

With the rapid development of social networks, online and offline group activities are
becoming more common and diverse. Considering the different interests of group members …

This special issue presents robust, explainable, and efficient next-generation deep learning
algorithms with data privacy and theoretical guarantees will improve the understanding and …

In a high variety of application domains, the amount of data generated daily has grown more
and more over time to the point that its use now exceeds the computational capacity of …

In the realm of electronic commerce recommendation systems, the search methodology
assumes a critical role in shaping consumers' purchasing determinations. Against a …