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Diverse Retrieval via Greedy Optimization of Expected 1-call@k in a Latent Subtopic Relevance Model

Scott Sanner, Shengbo Guo, Thore Graepel, Sadegh Kharazmi, and Sarvnaz Karimi

Abstract

It has been previously observed that optimization of the 1-call@k relevance objective (i.e., a set-based objective that is 1 if at least one document is relevant, otherwise 0) empirically correlates with diverse retrieval. In this paper, we proceed one step further and show theoretically that greedily optimizing expected 1-call@k w.r.t. a latent subtopic model of binary relevance leads to a diverse retrieval algorithm sharing many features of existing diversification approaches. This new result is complementary to a variety of diverse retrieval algorithms derived from alternate rank-based relevance criteria such as average precision and reciprocal rank. As such, the derivation presented here for expected 1-call@k provides a novel theoretical perspective on the emergence of diversity via a latent subtopic model of relevance — an idea underlying both ambiguous and faceted subtopic retrieval that have been used to motivate diverse retrieval.

Details

Publication typeInproceedings
Published inIn Proceedings of CIKM, 20th ACM Conference on Information and Knowledge Management
PublisherACM
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