Text Document

Augmenting Recommender Systems by Embedding Interfaces into Practices

Fulltext URI

Document type

Additional Information



Journal Title

Journal ISSN

Volume Title


Association for Computing Machinery


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.


Grasso, Antonietta; Koch, Michael; Rancati, Alessandro (1999): Augmenting Recommender Systems by Embedding Interfaces into Practices. Proceedings of the 1999 ACM International Conference on Supporting Group Work. DOI: 10.1145/320297.320329. New York, NY, USA: Association for Computing Machinery. pp. 267–275. Phoenix, Arizona, USA