Recommender systems have become an important research field since the emergence of
the first paper on collaborative filtering in the mid-1990s. Although academic research on …

The success of new scientific areas can be assessed by their potential in contributing to new
theoretical approaches and in applications to real-world problems. Complex networks have …

Recommender systems have developed in parallel with the web. They were initially based
on demographic, content-based and collaborative filtering. Currently, these systems are …

The integration of social networking concepts into the Internet of things has led to the Social
Internet of Things (SIoT) paradigm, according to which objects are capable of establishing …

Because effective influencers in an online social network (OSN) can significantly affect
consumers' purchasing decisions via trust among users in electronic word-of-mouth (eWOM) …

The new user cold start issue represents a serious problem in recommender systems as it
can lead to the loss of new users who decide to stop using the system due to the lack of …

Deep neural networks have been extensively employed in many applications such as
natural language processing and computer vision. They have attracted a lot of attention in …

Abstract Internet of Things (IoT) creates a world where smart objects and services interacting
autonomously. Taking into account the dynamic-heterogeneous characteristic of …

We propose TrustSVD, a trust-based matrix factorization technique for recommendations.
TrustSVD integrates multiple information sources into the recommendation model in order to …

Intelligent data handling techniques are beneficial for users; to store, process, analyze and
access the vast amount of information produced by electronic and automated devices. The …