New Techniques in Intelligent Information Filtering

OF THE DISSERTATION New Techniques in Intelligent Information Filtering by Sofus Attila Macskassy Dissertation Director: Dr. Haym Hirsh 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 predictive model is only as good as the trust that its user puts in it. Many predictive models are opaque, in the sense that they are not easily understood or explained to a human. Thus, I introduce a technique for taking an opaque model and generating a small set of rules that attempt to replicate its performance. I show that the rules generated by my technique on the five case studies are plausible representations