In the era of Internet of Things (IoT), millions of heterogeneous IoT devices generate an
explosion of data and services waiting to be discovered. The convergence of IoT with social …

Deep learning is the most active research topic amongst data scientists and analysts these
days. It is because deep learning has provided very high accuracy in various domains such …

Recently, there is notable development in usage of online learning resources by the
learners. Increasing offerings of online materials to student creates complexity to locate …

Graph neural networks have been clearly proven to be powerful in recommendation tasks
since they can capture high-order user-item interactions and integrate them with rich …

Groups where like-minded people gather to share interests, comments, or participate in
activities have recently gained popularity in well-known social platforms, such as Meetup …

In order to effectively solve the problem of new items and obviously improve the accuracy of
the recommended results, we proposed a collaborative recommendation algorithm based …

Product recommendation has played an important role in improving user experiences and
obtaining more profits. To optimize recommendation models, pairwise learning has become …

In this article, we propose the PTP-MF (Pairwise Trust Prediction through Matrix
Factorisation) algorithm, an approach to predicting the intensity of trust and distrust relations …

The recommendation system has become the primary tool used by many Internet application
platforms to solve the problem of information overload, and it faces issues such as data …

Existing collaborative filtering algorithms suffer from the problem of data sparsity. Imputation-
based methods are promising algorithms, which alleviate data sparsity without using side …