Augmenting Recommender Systems by Embedding Interfaces into Practices
Antonietta Grasso, Michael Koch, Alessandro Rancati
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.
Proceedings of GROUP'99, 14-17 November, Phoenix, Arizona, US
group99.zip (756.76 kB)