Advanced Hidden Markov Models for Recognizing Search Phases
Sebastian Dungs, Norbert Fuhr · 2017
Although cognitive IR approaches usually distinguish between different search phases, there are no automatic methods for recognizing these phases. In this paper, we use constrained Hidden Markov Models (HMM), for addressing this issue. Especially, we develop a hybrid form of HMM combining both discrete and continuous signal values, which improves the recognition process. Furthermore, we show how the new model can be used for predicting the time to the next relevant document, which is a prerequisite for the application of the interactive probability ranking principle.