In the last 16 years, more than 200 research articles were published about research-paper
recommender systems. We reviewed these articles and present some descriptive statistics in …

Industry 4.0 have witnessed a paradigm shift from cyber-physical systems (CPS) that aims at
massive automation, to a more customer-driven approach. The shift has been attributed to …

The integration of artificial intelligence (AI) into daily life, particularly through information
retrieval and recommender systems, has necessitated advanced user modeling and …

The proliferation of e-commerce sites and online social media has allowed users to provide
preference feedback and maintain profiles in multiple systems, reflecting a variety of their …

We propose a new platform for user modeling with blockchains that allows users to share
data without losing control and ownership of it and applied it to the domain of travel booking …

In order to adapt functionality to their individual users, systems need information about these
users. The Social Web provides opportunities to gather user data from outside the system …

The emergence of social networks and the vast amount of data that they contain about their
users make them a valuable source for personal information about users for recommender …

As technology has become ubiquitous in learning contexts, there has been an explosion in
the amount of learning data. This creates opportunities to draw on the decades of learner …

Widespread personalized computing systems play an already important and fast-growing
role in diverse contexts, such as location-based services, recommenders, commercial Web …

With the growing popularity of microblogging services such as Twitter in recent years, an
increasing number of users are using these services in their daily lives. The huge volume of …