The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Page quality: in search of an unbiased web ranking
Abstract
In a number of recent studies [4, 8] researchers have found that because search engines repeatedly return currently popular pages at the top of search results, popular pages tend to get even more popular, while unpopular pages get ignored by an average user. This "rich-get-richer" phenomenon is particularly problematic for new and high-quality pages because they may never get a chance to get users' attention, decreasing the overall quality of search results in the long run. In this paper, we propose a new ranking function, called page quality that can alleviate the problem of popularity-based ranking. We first present a formal framework to study the search engine bias by discussing what is an "ideal" way to measure the intrinsic quality of a page. We then compare how PageRank, the current ranking metric used by major search engines, differs from this ideal quality metric. This framework will help us investigate the search engine bias in more concrete terms and provide clear understanding why PageRank is effective in many cases and exactly when it is problematic. We then propose a practical way to estimate the intrinsic page quality to avoid the inherent bias of PageRank. We derive our proposed quality estimator through a careful analysis of a reasonable web user model, and we present experimental results that show the potential of our proposed estimator. We believe that our quality estimator has the potential to alleviate the rich-get-richer phenomenon and help new and high-q…
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“[Cho et al. 2005] relied on web link structure, especially its evolution, to propose a new ranking metric to solve the problem pointed out by [Baeza-Yates et al. 2002]”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Cho et al. 2005
Citations
Cites
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Cited by
Sorted by citations
Survey of Temporal Information Retrieval and Related Applications
Campos, R., Dias, G., Jorge, A. M., & Jatowt, A. (2015) · ACM Computing Surveys · doi:10.1145/2619088 · Cited by 262
Health 2.0 and Medicine 2.0: Tensions and Controversies in the Field
Hughes, B., Joshi, I., & Wareham, J. (2008) · Journal of Medical Internet Research · doi:10.2196/jmir.1056 · Cited by 221
Enhancing credibility judgment of web search results
Yamamoto, Y., & Tanaka, K. (2011) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979126 · Cited by 75
Metadata
- Authors
- Cho, J., Roy, S., & Adams, R. E.
- Affiliations
- UCLA Computer Science
- Venue
- Proceedings of the 2005 ACM SIGMOD international conference on Management of data
- Publisher
- ACM
- Published
- 2005-06-14
- Pages
- 551-562
- Cited by
- 69
- References
- 1
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