An Effective User Profiling Data Structure for Dynamic License

In this work, we defined and constructed an effective user profiling data structure for the dynamic license of digital contents. The user profiling data structure can be deduced from the analysis on existing digital music license purchase pattern included in the user profiling data. It is designed to trace the user's license purchase pattern and can be used to analyze and trace the user's license access pattern with the history based aged-MRU algorithm. Since dynamic license has more complicate features on describing license actions and relations with other persons than static license, the user profiling data structure also should have more member items in metadata. By comparing the difference of static license features with dynamic license features, we can validate the user profiling data structure for dynamic license and can get the effectiveness of the data structure on license purchase prediction of the dynamic license as like the static license. In order to construct the user profiling data structure for the dynamic license, several metadata items should be added to the user profiling data structure for the static license. In this work, we proposed a kind of simple user profiling data structure for dynamic license and evaluated its effectiveness of the next user license purchase prediction with the aged-MRU algorithm. The evaluation results show that the proposed user profiling data structure for the dynamic license can present the related metadata to the license purchase and can predict the next license purchase activity in dynamic license environment.