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 …

A recommender system is a Web technology that proactively suggests items of interest to
users based on their objective behavior or explicitly stated preferences. Evaluations of …

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 …

Scholars in algorithmic recommender systems research have developed a largely
standardized scientific method, where progress is claimed by showing that a new algorithm …

Traditionally, the field of recommender systems has evaluated the fruits of its labor using
metrics of algorithmic accuracy and precision (see Chap. 8 for an overview of recommender …

Research on recommender systems typically focuses on the accuracy of prediction
algorithms. Because accuracy only partially constitutes the user experience of a …

In the research literature, evaluations of recommender system effectiveness typically report
results over a given data set, providing an aggregate measure of effectiveness over each …

Recommendation has become a prominent area of research in the field of Information
Retrieval (IR). Evaluation is also a traditional research topic in this community. Motivated by …

Recommender systems attempt to reduce information overload and retain customers by
selecting a subset of items from a universal set based on user preferences. While research …

This research was motivated by our interest in understanding the criteria for measuring the
success of a recommender system from users' point view. Even though existing work has …