Recommender systems are an integral part of the online retail environment. Prior research has focused largely on computational approaches to improving recommendation accuracy, and only recently researchers have started to study their behavioral implications and potential side effects. We used...

The idea behind the methodology proposed in this paper is to show the utilities that it can offer, a software tool to make recommendations to the banking users when making a claim against a bad practice from their banks or wish to make an inquiry in order to ascertain the chances of a favorable...

Recommender systems help people to find information that is interesting to them. However, current recommendation techniques only address the user's short-term and long-term interests, not their immediate interests. This paper describes a method to structure information (with or without using...

Predicting the interests of a user in information is an important process in personalized information systems. In this paper, we present a way to create prediction engines that allow prediction techniques to be easily combined into prediction strategies. Prediction strategies choose one or a...

It has been conjectured that the peer-based recommendations associated with electronic commerce lead to a redistribution of demand from popular products or "blockbusters" to less popular or "niche" products, and that electronic markets will therefore be characterized by a "long tail" of demand...