Abstract
We live in the information overload age. Don’t believe that? Here is some evidence: The world’s total yearly production of print, film, optical, and magnetic content would require roughly 1.5 billion gigabytes of storage. This is the equivalent of 250 megabytes per person for each man, woman, and child on earth. (Lyman and Varian, 2000) The massive amount of content produced each day is changing the way each of us lives our life. Historically, society has coped with the problem of too much information by employing editors, reviewers, and publishers to separate the signal from the noise. The problem is that we do not have enough editors, publishers, and reviewers to keep up with the volume of new content. One solution to this problem is to use technology to allow each of us to act as an editor, publisher, and reviewer for some subset of the rest of society. The technology that enables us to work together to solve the information overload problem for each other is called collaborative filtering.
Keywords
- Receiver Operating Characteristic Curve
- Recommender System
- News Article
- Collaborative fIltering
- Information Item
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Miller, B.N., Ried, J.T., Konstan, J.A. (2003). GroupLens for Usenet: Experiences in Applying Collaborative Filtering to a Social Information System. In: Lueg, C., Fisher, D. (eds) From Usenet to CoWebs. Computer Supported Cooperative Work. Springer, London. https://doi.org/10.1007/978-1-4471-0057-7_10
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DOI: https://doi.org/10.1007/978-1-4471-0057-7_10
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