Intelligence is the ability to learn from experience and use domain experts' knowledge to
adapt to new situations. In this context, an intelligent Recommender System should be able …

Personalized recommender systems have become indispensable in today's online world.
Most of today's recommendation algorithms are data-driven and based on behavioral data …

Recommender systems are tools for interacting with large and complex information spaces.
They provide a personalized view of such spaces, prioritizing items likely to be of interest to …

The origins of modern recommender systems date back to the early 1990s when they were
mainly applied experimentally to personal email and information filtering. Today, 30 years …

In this age of information overload, people use a variety of strategies to make choices about
what to buy, how to spend their leisure time, and even whom to date. Recommender …

Recommender systems have already proved to be valuable for coping with the information
overload problem in several application domains. They provide people with suggestions for …

Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

Recommender systems are assisting users in the process of identifying items that fulfill their
wishes and needs. These systems are successfully applied in different e-commerce settings …

Psychological factors such as personality, emotions, social connections, and decision
biases can significantly affect the outcome of a decision process. These factors are also …

Context-aware recommender systems are information filtering and decision support
applications that generate recommendations by exploiting context-dependent user …