Augmenting Recommender Systems by Embedding Interfaces into Practices

dc.contributor.authorGrasso, Antonietta
dc.contributor.authorKoch, Michael
dc.contributor.authorRancati, Alessandro
dc.date.accessioned2023-06-08T11:41:48Z
dc.date.available2023-06-08T11:41:48Z
dc.date.issued1999
dc.description.abstractAutomated 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.doi10.1145/320297.320329
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4757
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofProceedings of the 1999 ACM International Conference on Supporting Group Work
dc.subjectrecommender system
dc.subjectpaper interface
dc.titleAugmenting Recommender Systems by Embedding Interfaces into Practicesen
gi.citation.publisherPlaceNew York, NY, USA
gi.citation.startPage267–275
gi.conference.locationPhoenix, Arizona, USA

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