LIFAT Seminar: Patrick Marcel - Profiling user belief in BI exploration for measuring subjective interestingness
on the May 23, 2019
Patrick Marcel , Nicolas Labroche
Title: Profiling user belief in BI exploration for measuring subjective interestingness. PDF
This paper addresses the long-term problem of defining a subjective interestingness measure for BI exploration. Such a measure involves prior modeling of the belief of the user. The complexity of this problem lies in the impossibility to ask the user about the degree of belief in each element composing their knowledge prior to the writing of a query. To this aim, we propose to automatically infer this user belief based on the user’s past interactions over a data cube, the cube schema and other users’ past activities. We express the belief under the form of a probability distribution over all the query parts potentially accessible to the user. This distribution is learned using a random walk approach, and more specifically an adapted topic-specific PageRank. The resulting belief provides the foundations for the definition of subjective interestingness measures that can be use to improve the user’s experience in their explorations. In the absence of ground truth for user belief, we simulate in our tests different users and their belief distributions with artificial cube explorations and evaluate our proposal based on qualitative evaluation. We finally propose a preliminary usage of our belief estimation in the context of query recommendation
