Recommender systems are widely used online to support users in finding relevant information. They can be based on different techniques such as content-based and collaborative filtering. In this paper, we introduce a new way of similarity calculation for item-based collaborative filtering. Thereby we focus on the usage of an object and not on the object's users as we claim the hypothesis that similarity of usage indicates content similarity. To prove this hypothesis we use learning objects accessible through the MACE portal where students can query several architectural repositories. For these objects, we generate object profiles based on their usage monitored within MACE. We further propose several recommendation techniques to apply this usagebased similarity calculation in real systems.

attention metadata, recommender systems, item-based collaborative filtering