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Dynamic Ranked Retrieval

Brandt, Christina and Joachims, Thorsten and Yue, Yisong and Bank, Jacob (2011) Dynamic Ranked Retrieval. In: Proceedings of the 4th ACM international conference on Web search and data mining. ACM , New York, NY, pp. 247-256. ISBN 978-1-4503-0493-1.

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We present a theoretically well-founded retrieval model for dynamically generating rankings based on interactive user feedback. Unlike conventional rankings that remain static after the query was issued, dynamic rankings allow and anticipate user activity, thus providing a way to combine the otherwise contradictory goals of result diversification and high recall. We develop a decision-theoretic framework to guide the design and evaluation of algorithms for this interactive retrieval setting. Furthermore, we propose two dynamic ranking algorithms, both of which are computationally efficient. We prove that these algorithms provide retrieval performance that is guaranteed to be at least as good as the optimal static ranking algorithm. In empirical evaluations, dynamic ranking shows substantial improvements in retrieval performance over conventional static rankings.

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Additional Information:© 2011 ACM. This work was funded in part by NSF Award IIS-0905467. The third author was also funded in part by a Microsoft Research Graduate Fellowship. The authors thank Robert Kleinberg for valuable discussions regarding this work.
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Microsoft ResearchUNSPECIFIED
Subject Keywords:Diversified Retrieval, Relevance Feedback, Decision Theory
Record Number:CaltechAUTHORS:20140910-134106609
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:49551
Deposited By: Tony Diaz
Deposited On:10 Sep 2014 21:56
Last Modified:03 Oct 2019 07:14

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