The wide proliferation of various wireless communication systems and devices has led to the arrival of a massive amount of Digital Resources (DR) from multi-sources, various metadata and media. However, data integration has allowed the ability to provide to users a uniform interface for multiple heterogonous data sources, metadata and users. Hence, the problem of matching which contents or DR belong to a specific user interest that demands more attention. In this article, we proposed a different model named: Learning & Boosting Architecture Model (LBAM). LBAM has goals to identify evolving interests of a person and to potentially propose a personal agenda, channels and activities. The first process is based on the creation of a hub of multiple sources of Micro Metadata (MM) using a Semantic Enriched MM Harvestor, a Watch & Notify Engine and a Semantic Shared Knowledge Notice (SSKN). They are harvested through a process able to catalogue the rights, interests and novelties in a scorm notice. It uses Machine Learning Models to improve the auto cataloguing of the DRs. It includes a Semantic Learning Watch and Notify engine using SSKN that allows ways to find DR or Event novelties of DR according to the evolving user interests. Using simulation studies and prototypes, we demonstrate that LBAM slightly improves accuracy in harvesting treatment from Entity Resolution and Linked Data compared to existing models using SSKN. We also demonstrate the integration of MM rights in a notice compared to other existing architectures. This article is the first paper of multiple for the LBAM project.
