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Search result diversification via data fusion

Published:03 July 2014Publication History

ABSTRACT

In recent years, researchers have investigated search result diversification through a variety of approaches. In such situations, information retrieval systems need to consider both aspects of relevance and diversity for those retrieved documents. On the other hand, previous research has demonstrated that data fusion is useful for improving performance when we are only concerned with relevance. However, it is not clear if it helps when both relevance and diversity are both taken into consideration. In this short paper, we propose a few data fusion methods to try to improve performance when both relevance and diversity are concerned. Experiments are carried out with 3 groups of top-ranked results submitted to the TREC web diversity task. We find that data fusion is still a useful approach to performance improvement for diversity as for relevance previously.

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    • Published in

      cover image ACM Conferences
      SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval
      July 2014
      1330 pages
      ISBN:9781450322577
      DOI:10.1145/2600428

      Copyright © 2014 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 3 July 2014

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      Acceptance Rates

      SIGIR '14 Paper Acceptance Rate82of387submissions,21%Overall Acceptance Rate792of3,983submissions,20%

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