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 …

If we assume that an important function of recommender systems is to help people make
better choices, it follows that people who design and study recommender systems ought to …

In order to build relevant, useful, and effective recommender systems, researchers need to
understand why users come to these systems and how users judge recommendation lists …

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 …

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 …

We present a novel approach that integrates algorithmic recommender techniques with
interactive faceted filtering methods. We refer to this approach as blended recommending. It …

In contrast to customers of bricks and mortar stores, users of online selling environments are
not supported by human sales experts. In such situations recommender applications help to …

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 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 …

Automated recommender systems predict user preferences by applying machine learning
techniques to data on products, users, and past user preferences for products. Such systems …