Automated collaborative filtering systems promote the creation of a meta-layer of information describing the usersâ evaluation of the quality and relevance of information items like scientific papers, books, and movies. A rich meta-layer is needed in these systems in order to statistically elaborate good predictions of the interest of the information items; this makes the extensiveness of usersâ contribution of feedback an essential aspect to have these systems to produce good prediction quality. The work presented here first analyses the issues around recommendation collection, then proposes a set of design principles for improving the collection of recommendations, and finally presents how these principles have been applied in two settings.    «

Automated collaborative filtering systems promote the creation of a meta-layer of information describing the usersâ evaluation of the quality and relevance of information items like scientific papers, books, and movies. A rich meta-layer is needed in these systems in order to statistically elaborate good predictions of the interest of the information items; this makes the extensiveness of usersâ contribution of feedback an essential aspect to have these systems to produce good prediction qualit...    »