New techniques in intelligent information filtering
Sofus A. Macskassy, Haym Hirsh · 2003
Intelligent Information Filtering is the process of receiving or monitoring large amounts of dynamically generated information and extracting the subset of information that would be of interest to a user based on some specified information need. Historically, this need has been based on user profiles that are directly evaluable---the information can be immediately classified as interesting or not. In this thesis I introduce a new type of user interestingness criterion which is prospective---the criterion defines the interestingness of an information item based on events that happen subsequent to the information item appearing. Hence, the interestingness cannot be directly evaluated. A new technique is described which takes such a criterion and operationalizes it, using machine learning to generate a predictive model that can directly evaluate a piece of information. I show that this technique works statistically significantly better than the baseline of predicting based on class distribution on five information filtering case studies. However, a