Collaborative filtering and inference rules for context-aware learning object recommendation

Learning objects strive for reusability in e-Learning to reduce cost and allow personalization of content. We argue that learning objects require adapted Information Retrieval systems. In the spirit of the Semantic Web, we discuss the semantic description, discovery, and composition of learning objects using Web-based MP3 objects as examples. As part of our project, we tag learning objects with both objective and subjective metadata. We study the application of collaborative filtering as prototyped in the RACOFI (Rule-Applying Collaborative Filtering) Composer system, which consists of two libraries and their associated engines: a collaborative filtering system and an inference rule system. We are currently developing RACOFI to generate context-aware recommendation lists. Context is handled by multidimensional predictions produced from a database-driven scalable collaborative filtering algorithm. Rules are then applied to the predictions to customize the recommendations according to user profiles. The prototype is available at inDiscover.net