Evaluation of result merging strategies for metasearch engines

  • Authors:

  • Yiyao Lu;Weiyi Meng;Liangcai Shu;Clement Yu;King-Lup Liu

  • Affiliations:

  • Dept of Computer Science, SUNY at Binghamton, Binghamton, NY;Dept of Computer Science, SUNY at Binghamton, Binghamton, NY;Dept of Computer Science, SUNY at Binghamton, Binghamton, NY;Dept of Computer Science, U. of Illinois at Chicago, Chicago, IL;Webscalers, LLC, Lafayette, LA

  • Venue:
  • WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
  • Year:
  • 2005

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Abstract

Result merging is a key component in a metasearch engine. Once the results from various search engines are collected, the metasearch system merges them into a single ranked list. The effectiveness of a metasearch engine is closely related to the result merging algorithm it employs. In this paper, we investigate a variety of resulting merging algorithms based on a wide range of available information about the retrieved results, from their local ranks, their titles and snippets, to the full documents of these results. The effectiveness of these algorithms is then compared experimentally based on 50 queries from the TREC Web track and 10 most popular general-purpose search engines. Our experiments yield two important results. First, simple result merging strategies can outperform Google. Second, merging based on the titles and snippets of retrieved results can outperform that based on the full documents.