Evaluation of Result Merging Strategies for Metasearch Engines

Autor:	Clement Yu, Weiyi Meng, Liangcai Shu, Yiyao Lu, King-Lup Liu
Rok vydání:	2005
Předmět:	Information retrieval business.industry Computer science InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Search aggregator computer.software_genre Search engine Range (mathematics) Component (UML) Key (cryptography) Information system The Internet Data mining business Metasearch engine computer
Zdroj:	Lecture Notes in Computer Science ISBN: 9783540300175 WISE
DOI:	10.1007/11581062_5
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d8316a6fd59313b4488a957052166aa6 https://doi.org/10.1007/11581062_5 Zobrazit plný text záznamu