Reducing the Complexity of Recommender Systems Development

Abstract

At present, recommender systems are emerging as a growing application and research field in several domains of computing research, from artificial intelligence to information systems. In the past, these systems have been primarily used to reduce information overload and to identify the items that are of interest to the user more precisely. Recommender systems development is a complex task, on the other hand, abstraction and modularity are powerful concepts for handling the complexity of software development, especially if the problem domain is particularly complex, changeable, or large scale. This paper presents our attempt to reduce the complexity of recommender systems development via using software architecture concepts as well as multi agent system.