Augmenting Recommender Systems by Embedding Interfaces into Practices

Abstract

Automated 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.