Automated collaborative filtering systems collect evaluations from users of the quality and relevance of stored information items, such 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.