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Graph-Based Email Prioritization.

Overview

The exponential growth of the Internet over the last two decades has raised a number of issues. Unsolicited bulk email, or spam, has become a huge burden on individuals and businesses alike. Typically sent out in mass quantities, many approaches have been taken to fight email spam. From simple text-based filters, to whitelists and blacklists, and increasingly complex Bayesian learners, these approaches have met with varying degrees of success. The ubiquity of email presents a second problem. An individual may ...
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Overview

The exponential growth of the Internet over the last two decades has raised a number of issues. Unsolicited bulk email, or spam, has become a huge burden on individuals and businesses alike. Typically sent out in mass quantities, many approaches have been taken to fight email spam. From simple text-based filters, to whitelists and blacklists, and increasingly complex Bayesian learners, these approaches have met with varying degrees of success. The ubiquity of email presents a second problem. An individual may receive tens or hundreds of legitimate email messages per day. Whether this excess email is legitimate or not is unimportant once it becomes an unreasonable burden on ones time. It must still be filtered, or better yet ranked, so that valuable time is not wasted. This thesis uses graph-based methods to prioritize incoming email messages. A model is first constructed from the header information of previously received messages. The model is then used to predict which email messages in a user's inbox are most likely to be urgent and in need of a response. Once ranked, a user may read as many of the higher priority messages as time permits. Lower priority messages are ignored or saved until later. In the first part, a model is created for each user solely from that user's email history. In the second part, the model for each user incorporates the email histories of other users as well. Results are generated from tests using the Enron email dataset.
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Product Details

  • ISBN-13: 9781243399205
  • Publisher: BiblioLabsII
  • Publication date: 9/1/2011
  • Pages: 76
  • Product dimensions: 7.44 (w) x 9.69 (h) x 0.16 (d)

Meet the Author

Tao Lin
Tao Lin

Tao Lin was born in 1983, and raised in Orlando, Florida. In 2007 Melville House published his first two works of fiction, the short story collection Bed, and the novel Eeeee Eee Eeee, simultaneously. Lin quickly became an underground sensation with a huge cult following. In 2008, Lin published his poetry collection, Cognitive-Behavioral Therapy. It has been assigned as a text book in several college level psychology courses.

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