Information filtering: Overview of issues, research and systems

An abundant amount of information is created and delivered over electronic media. Users risk becoming overwhelmed by the flow of information, and they lack adequate tools to help them manage the situation. Information filteri ng (IF) is one of the methods that is rapidly evolving to manage large info rmation flows. The aim of IF is to expose users to only information that is relevant to them. Many IF systems have been developed in recent years for various application domains. Some examples of filtering applications are: f ilters for search results on the internet that are employed in the Internet software, personal e-mail filters based on personal profiles, listservers or newsgroups filters for groups or individuals, browser filters that block non-valuable information, filters designed to give children access them on ly to suitable pages, filters for e-commerce applications that address prod ucts and promotions to potential customers only, and many more. The differe nt systems use various methods, concepts, and techniques from diverse resea rch areas like: Information Retrieval, Artificial Intelligence, or Behavior al Science. Various systems cover different scope, have divergent functiona lity, and various platforms. There are many systems of widely varying philo sophies, but all share the goal of automatically directing the most valuabl e information to users in accordance with their User Model, and of helping them use their limited reading time most optimally. This paper clarifies the difference between IF systems and related systems, such as information retrieval (IR) systems, or Extraction systems. The pap er defines a framework to classify IF systems according to several paramete rs, and illustrates the approach with commercial and academic systems. The paper describes the underlying concepts of IF systems and the techniques th at are used to implement them. It discusses methods and measurements that a re used for evaluation of IF systems and limitations of the current systems . In the conclusion we present research issues in the Information Filtering research arena, such as user modeling, evaluation standardization and inte gration with digital libraries and Web repositories.