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ECIR
2011
Springer

Learning Models for Ranking Aggregates

8 years 1 months ago
Learning Models for Ranking Aggregates
Aggregate ranking tasks are those where documents are not the final ranking outcome, but instead an intermediary component. For instance, in expert search, a ranking of candidate persons with relevant expertise to a query is generated after consideration of a document ranking. Many models exist for aggregate ranking tasks, however obtaining an effective and robust setting for different aggregate ranking tasks is difficult to achieve. In this work, we propose a novel learned approach to aggregate ranking, which combines different document ranking features as well as aggregate ranking approaches. We experiment with our proposed approach using two TREC test collections for expert and blog search. Our experimental results attest the effectiveness and robustness of a learned model for aggregate ranking across different settings.
Craig Macdonald, Iadh Ounis
Added 27 Aug 2011
Updated 27 Aug 2011
Type Journal
Year 2011
Where ECIR
Authors Craig Macdonald, Iadh Ounis
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