Recommender Systems are the set of tools and techniques to provide useful
recommendations and suggestions to the users to help them in the decision-making process …

Reliable data measurement is considered to be one of the critical ingredients for variant
Internet of Things (IoT) applications. Gaining full knowledge of measurement data is …

Context-aware recommender systems dedicated to online social networks experienced
noticeable growth in the last few years. This has led to more research being done in this …

Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …

Tensor-based methods have shown promise in improving upon traditional matrix
factorization methods for recommender systems. But tensors may achieve improved …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

Recommender systems are a classical example for machine learning applications, however,
they have not yet been used extensively in health informatics and medical scenarios. We …

Tensor completion has become an effective computational tool in many real-world data-
driven applications. Beyond traditional static setting, with the increasing popularity of high …

A growing community of researchers has been investigating the equity of algorithms,
advancing the understanding of risks and opportunities of automated decision-making for …

Attribute-aware CF models aim at rating prediction given not only the historical rating given
by users to items but also the information associated with users (eg, age), items (eg, price) …