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Enormous volumes of email are proliferated around the world every day, and a significant number of users believe that a large proportion of their time is being wasted dealing with the resultant inbox 'flooding'. This research studies existing email classification techniques which aim to reduce the...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2024
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| Summary: | Enormous volumes of email are proliferated around the world every day, and a significant number of users believe that a large proportion of their time is being wasted dealing with the resultant inbox 'flooding'. This research studies existing email classification techniques which aim to reduce the burden of the burgeoning inbox. It then develops the first iteration of a prototype which makes use of sender-assisted classification techniques, having used HCI investigative techniques to develop a set of improvements to existing email clients. This prototype is then evaluated. The suggested improvements are incorporated into a second prototype based on the recommendations from the first prototype. |
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