Dynamic Email Organization via Relevance Categories

Many researchers have proposed classification systems that automatically classify email in order to reduce information overload. However, none of these systems are in use today. This paper examines some of the problems with classification technologies and proposes Relevance Categories as a method to avoid some of these problems. In particular, the dynamic nature of email categories, the cognitive overhead required to train categories, and the high costs of classification errors are hurdles for many classification algorithms. Relevance Categories avoid some of these problems through their simplicity; they are merely relevanceranked lists of email messages that are similar to a set of query messages. By displaying messages as dynamic query results in lieu of fixed categories, we hypothesize that users will be less sensitive to errors in the Relevance Categories scheme than to errors in a fixed categorization scheme. To study the effectiveness of the Relevance Categories concept, we devi...

• Type: Conference
• Year: 1999
• Where: ICTAI
• Authors: Kenrick J. Mock
• Keywords: Artificial Intelligence, Classification Errors, Email Categories, ICTAI 1999, Relevance Categories