Ranking Web Pages Using Machine Learning Approaches

Yong, Sweah Liang; Hagenbuchner, Markus; Tsoi, Ah Chung

doi:10.1109/wiiat.2008.235

Cited by 17 publications

(2 citation statements)

References 10 publications

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“…This may be possible through a machine learning approach. In particular, recent research extended the ability of machine learning to allow the encoding of structured, Web‐based information in either an unsupervised fashion 31 or a supervised fashion 32.…”

Section: Discussionmentioning

confidence: 99%

Automated functional testing of online search services

Zhi

Zhang

Hagenbuchner

et al. 2010

Software Testing Verif & Rel

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SUMMARYSearch services are the main interface through which people discover information on the Internet. A fundamental challenge in testing search services is the lack of oracles. The sheer volume of data on the Internet prohibits testers from verifying the results. Furthermore, it is difficult to objectively assess the ranking quality because different assessors can have very different opinions on the relevance of a Web page to a query. This paper presents a novel method for automatically testing search services without the need of a human oracle. The experimental findings reveal that some commonly used search engines, including Google, Yahoo!, and Live Search, are not as reliable as what most users would expect. For example, they may fail to find pages that exist in their own repositories, or rank pages in a way that is logically inconsistent. Suggestions are made for search service providers to improve their service quality.

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