Augmenting recommender systems by embedding interfaces into practices

Automated collaborative filtering systems collect evaluations from users of the quality and relevance of stored information items, arch as scientific papers, books, and movies. A number of users need to give evaluations for the systems to be able to produce statistically high quality predictions of an item´s interest. Promoting the creation of a rich meta-layer of evaluations is essential for these systems, but several important issues remain to be resolved. The work presented here first analyses the issues around the collection of recommendations, then proposes a set of design principles for improving and automating the collection of recommendations, and finally presents how these principles have been implemented in a real usage setting.