The development of IoT technologies and the massive admiration and acceptance of social
media tools and applications, new doors of opportunity have been opened for using data …

With the growth of online information, varying personalization drifts and volatile behaviors of
internet users, recommender systems are effective tools for information filtering to overcome …

Federated learning (FL) is quickly becoming the de facto standard for the distributed training
of deep recommendation models, using on-device user data and reducing server costs. In a …

Deciphering user purchase preferences, their likes and dislikes is a very tricky task even for
humans, making its automation a very complex job. This research work augments heuristic …

With the new era of the Internet, we have a large amount of data available in the form of
ratings, reviews, graphs, images, etc. However, still, people face difficulty in finding useful …

Recommender systems aim at predicting users' preferences based on abundant information,
such as user ratings, demographics, and reviews. Although reviews are sparser than ratings …

Crowdfunding provides an alternative way of financing, but its success is heavily challenged
by information asymmetry. Based on the signal theory, this paper investigates the impact of …

Nowadays, we can use different websites that help us make decisions about various aspects
of our lives. However, privacy protection prevents websites from providing personalised …

Scholars have been increasingly calling for innovative research in the organizational
sciences in general, and the information systems (IS) field in specific, one that breaks from …

Federated learning (FL) is a promising new technology that allows machine learning (ML)
models to be trained locally on edge devices while preserving the privacy of the devices' …