Services and engines for a recommender system

Abstract

The number of ecommerce websites has grown rapidly over the past few years. Most of these websites use a back end database and support clients by providing ordinary database query facilities. However, using this approach has the major drawback that searching for a required product becomes difficult: Often users are faced with situations were the database either returns too many results or none at all. Also these websites, regardless of the fact they are used by a large number of users, they rarely take advantage of its population. Recommender systems can help dealing with the mass of products that are available in the databases of online organizations. They take advantage of the users of a web site and exploit personalization technologies to recommend items of interest to what other have liked. To overcome the problems of information overload and help customers receive better suggestions, a Framework is being proposed that identifies a common set of services which software developers should find helpful when implementing a recommender system, and implement these services through engines which are powered by knowledge based technologies. The framework should then be used by developers in order to create recommender systems in a shorter time.