Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Exploiting query reformulations for web search result diversification
Proceedings of the 19th international conference on World wide web
On the choice of effectiveness measures for learning to rank
Information Retrieval
A comparative analysis of cascade measures for novelty and diversity
Proceedings of the fourth ACM international conference on Web search and data mining
Intent-aware search result diversification
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Measuring the coverage and redundancy of information search services on e-commerce platforms
Electronic Commerce Research and Applications
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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.