Editors: Francesco Ricci; Lior Rokach; Bracha Shapira; Paul B. Kantor

The explosive growth of e-commerce and online environments has made the
issue of information search and selection increasingly serious; users are
overloaded by options to consider and they may not have the time or
knowledge to personally evaluate these options. Recommender systems have
proven to be a valuable way for online users to cope with the information
overload and have become one of the most powerful and popular tools in
electronic commerce. Correspondingly, various techniques for recommendation
generation have been proposed, and during the last decade, many have been
successfully deployed in commercial environments.

Development of recommender systems is a multi-disciplinary effort that
involves world-wide experts from diverse fields, such as:

* Artificial intelligence,
* Human Computer Interaction,
* Information Technology,
* Data Mining,
* Statistics,
* Adaptive User Interfaces,
* Decision Support Systems,
* Marketing,
* or Consumer Behavior.

Theoreticians and practitioners from these fields continually seek techniques
for more efficient, cost-effective and accurate recommender systems.

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