The decision to examine a message at a particular point in time should be made rationally and economically if the message recipient is to operate efficiently. Electronic message distribution systems (e.g. email), electronic bulletin board systems, and telephone systems capable of leaving digitized voice messages (e.g., voicemail) can contribute to "information overload," defined as the economic loss associated with the examination of a number of non- or less-relevant messages, as in related information retrieval models. Our model provides a formal method for minimizing expected information overload through information filtering.
The proposed Bayesian filtering adaptive model predicts the usefulness of a message based on the available message features and may be useful to rank messages by expected importance or economic worth. The assumptions of binary and two Poisson independent probabilistic distributions of message feature frequencies are examined, and methods of incorporating these distributions into the ranking filter model are examined. Bayesian methods to incorporate user supplied relevance feedback are suggested. Analytic performance measures are proposed to predict system quality. Other message handling models, including rule based expert systems, are seen as special cases of the model. The performance is given for a set of unix shell programs which rank internet messages. Problems with the use of this formal model are examined, and areas for future research are suggested.
For additional technical discussion of this material, see Losee, Text Retrieval and Filtering: Analytic Models of Performance, Boston: Kluwer, 1998.
Losee home page at http://www.ils.unc.edu/~losee