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An optimally diverse ranking should achieve the maximum coverage of the aspects underlying an ambiguous or underspecified query, with minimum redundancy with respect to the covered aspects. Although evaluation metrics that reward coverage and penalise redundancy provide intuitive objective functions for learning a diverse ranking, it is unclear whether they are the most effective. In this paper, we contrast the suitability of relevance and diversity metrics as objective functions for learning a diverse ranking. Our results in the context of the diversity task of the TREC 2009 and 2010 Web tracks show that diversity metrics are not necessarily better suited for guiding a learning approach. Moreover, the suitability of these metrics is compromised as they try to penalise redundancy during the learning process.

On the suitability of diversity metrics for learning-to-rank for diversity