Grasso, AntoniettaKoch, MichaelRancati, Alessandro2023-06-082023-06-081999https://dl.eusset.eu/handle/20.500.12015/4757Automated collaborative filtering systems promote the creation of a meta-layer of information, which describes users' evaluations of the quality and relevance of information items like scientific papers, books, and movies. A rich meta-layer is required, in order to elaborate statistically good predictions of the interest of the information items; the number of users' contributing to the feedback is a vital aspect for 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 aimed at improving the collection of recommendations. Finally, it presents how these principles have been implemented in one real usage setting.enrecommender systempaper interface10.1145/320297.320329