Evaluating Expertise Recommendations

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dc.contributor.authorMcDonald, David W.
dc.date.accessioned2023-06-08T11:43:19Z
dc.date.available2023-06-08T11:43:19Z
dc.date.issued2001
dc.description.abstractFinding a person who has the expertise to solve a specific problem is an important application of recommender systems to a difficult organizational problem. Prior systems have made attempts to implement solutions to this problem, but few systems have undergone systematic user evaluation. This work describes a systematic evaluation of the Expertise Recommender (ER), a system that recommends people who are likely to have expertise in a specific problem. ER and the organizational context for which it was designed are described to provide a basis for understanding this evaluation. Prior to conducting the evaluation, a baseline experiment showed that people are relatively good at judging coworkers' expertise when given an appropriate context. This finding provides a way to demonstrate the effectiveness of ER by comparing ER's performance to ratings by coworkers. The evaluation, the design, and results are described in detail. The results suggest that the participants agree with the recommendations made by ER, and that ER significantly outperforms other expertise recommender systems when compared using similar metrics.en
dc.identifier.doi10.1145/500286.500319
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4785
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofProceedings of the 2001 ACM International Conference on Supporting Group Work
dc.subjectexpertise location
dc.subjectcomputer-supported cooperative work
dc.subjectCSCW
dc.subjectuser evaluation
dc.subjectrecommendation systems
dc.titleEvaluating Expertise Recommendationsen
gi.citation.publisherPlaceNew York, NY, USA
gi.citation.startPage214–223
gi.conference.locationBoulder, Colorado, USA

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