Personalization in Skipforward, an Ontology-Based Distributed Annotation System

Skipforward is a distributed annotation system allowing users to enter and browse statements about items and their features. Items can be things such as movies or books; item features are the genre of a movie or the storytelling pace of a book. Whenever multiple users annotate the same item with a statement about the same feature, these individual statements get aggregated by the system. For aggregation, individual user statements are weighted according to a competence metric based on the constrained Pearson correlation, adapted for Skipforward data: A user gets assigned high competence with regard to the feature in question if, for other items and the same feature type, he had a similar opinion to the current user. Since the competence metric is dependent on the user currently viewing the data, the user’s view of the data is completely personalized. In this paper, the personalization aspect as well as the item and expert recommender are presented.