Augmenting Recommender Systems by Embedding Interfaces into Practices
| dc.contributor.author | Grasso, Antonietta | |
| dc.contributor.author | Koch, Michael | |
| dc.contributor.author | Rancati, Alessandro | |
| dc.date.accessioned | 2023-06-08T11:41:48Z | |
| dc.date.available | 2023-06-08T11:41:48Z | |
| dc.date.issued | 1999 | |
| dc.description.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. | en |
| dc.identifier.doi | 10.1145/320297.320329 | |
| dc.identifier.uri | https://dl.eusset.eu/handle/20.500.12015/4757 | |
| dc.language.iso | en | |
| dc.publisher | Association for Computing Machinery | |
| dc.relation.ispartof | Proceedings of the 1999 ACM International Conference on Supporting Group Work | |
| dc.subject | recommender system | |
| dc.subject | paper interface | |
| dc.title | Augmenting Recommender Systems by Embedding Interfaces into Practices | en |
| gi.citation.publisherPlace | New York, NY, USA | |
| gi.citation.startPage | 267–275 | |
| gi.conference.location | Phoenix, Arizona, USA |