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Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Multimedia Information Filtering

Multimedia Information Filtering
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Author(s): Minaz J. Parmar (Brunel University, UK) and Marios C. Angelides (Brunel University, UK)
Copyright: 2009
Pages: 6
Source title: Encyclopedia of Information Science and Technology, Second Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-60566-026-4.ch439

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Abstract

In the film Minority Report (20th Century Fox, 2002), which is set in the near future, there is a scene where a man walks into a department store and is confronted by a holographic shop assistant. The holographic shop assistant recognises the potential customer by iris-recognition technology. The holographic assistant then welcomes the man by his name and starts to inform him of offers and items that he would be interested in based on his past purchases and what other shoppers who have similar tastes have purchased. This example of future personalised shopping assistants that can help a customer find shopping goods is not too far away from becoming reality in some form or another. Malone, Grant, Turbak, Brobst, and Cohen (1987) introduced three paradigms for information selection, cognitive, economic, and social, based on their work with a system they called the Information Lens. Their definition of cognitive filtering, the approach actually implemented by the Information Lens, is equivalent to the “content filter” defined earlier by Denning, and this approach is now commonly referred to as “content-based” filtering. Their most important contribution was to introduce an alternative approach that they called social (now also more commonly called collaborative) filtering. In social filtering, the representation of a document is based on annotations to that document made by prior readers of the document. In the 1990s much work was done on collaborative filtering (CF). There were three systems that were considered to be the quintessential recommender systems. The Grouplens project (Miller, Albert, Lam, Konstan, & Riedl, 2003) initially was used for filtering items from the Usenet news domain. This later became the basis of Movielens. The Bellcore Video recommender system (Hill, Stead, Rosenstein, & Furnas, 1995), which recommended video films to users based on what they had rented before, and Ringo (Shardanand & Maes, 1995), which later was published on the Web and marketed as Firefly, used social filtering to recommend movies and music.

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