A New Methodology to Bring Out Typical Users Interactions in Digital Libraries

With the growing amount of digital publications, digital libraries (DLs) attract a variety of users for diverse tasks. A practical need to investigate how users interact with digital library (DL) portals is greatly increasing. Modeling users' interaction in DLs is interestingly required in order to optimize the use of different DL functionalities and to ease the accessibility to stored resources. The aim of this work is to take advantage of Process Mining techniques to model DL user's journeys. To the best of our knowledge, no other research work applied PM to real DLs users journeys. Discovered models can therefore be used in forthcoming work to present a set of recommendations to DL users. However, the large number of generated logs leads to complicated models that are not generic for all users and do not allow achieving all their objectives. For this reason, we propose in this paper a new methodology of grouping users' interactions prior to modeling. We compare our proposed approach to two state-of-the-art methods over a synthetic resource manually annotated used for validation and a real-life user interaction history (event logs) provided by the national library of France. The experimental part shows that our method outperforms existing methods in both clustering and modeling users over the synthetic dataset and generates interesting models on real-world data.