In previous research it has been shown that link-based web page metrics can be used to predict experts' assessment of quality. We are interested in a related question: do expert rankings of real-world entities correlate with search engine rankings of corresponding web resources? For example, each year US News & World Report publishes a list of (among others) top 50 graduate business schools. Does their expert ranking correlate with the search engine ranking of the URLs of those business schools? To answer this question we conducted 9 experiments using 8 expert rankings on a range of academic, athletic, financial and popular culture topics. We compared the expert rankings with the rankings in Google, Live Search (formerly MSN) and Yahoo (with list lengths of 10, 25, and 50). In 57 search engine vs. expert comparisons, only 1 strong and 4 moderate correlations were statistically significant. In 42 inter-search engine comparisons, only 2 strong and 4 moderate correlations were statistically significant. The correlations appeared to decrease with the size of the lists: the 3 strong correlations were for lists of 10, the 8 moderate correlations were for lists of 25, and no correlations were found for lists of 50.
@cite_12 observe a rich-get-richer'' phenomenon where popular pages tend to get even more popular since search engines repeatedly return popular pages first. As other studies by Cho @cite_10 @cite_21 and Baeza-Yates @cite_19 have shown, PageRank is significantly biased against new (and thus unpopular) pages which makes it problematic for these pages to draw the user's attention even if they are potentially of high quality. That means the popularity of a page can be much lower than its actual quality. propose page quality as an alternative ranking method. By defining quality of a web page as the probability that a user likes the page when seeing it for the first time the authors claim to be able to alleviate the drawbacks of PageRank. With the intuition from PageRank that a user that likes the page will link to it the algorithm is able to identify new and high quality pages much faster than PageRank and thus shorten the time it takes for them to get noticed.
@cite_19 @cite_21 @cite_10 @cite_12
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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-quality pages get the attention that they deserve. Algorithms that favor popular items are used to help us select among many choices, from top-ranked search engine results to highly-cited scientific papers. The goal of these algorithms is to identify high-quality items such as reliable news, credible information sources, and important discoveries–in short, high-quality content should rank at the top. Prior work has shown that choosing what is popular may amplify random fluctuations and lead to sub-optimal rankings. Nonetheless, it is often assumed that recommending what is popular will help high-quality content “bubble up” in practice. Here we identify the conditions in which popularity may be a viable proxy for quality content by studying a simple model of a cultural market endowed with an intrinsic notion of quality. A parameter representing the cognitive cost of exploration controls the trade-off between quality and popularity. Below and above a critical exploration cost, popularity bias is more likely to hinder quality. But we find a narrow intermediate regime of user attention where an optimal balance exists: choosing what is popular can help promote high-quality items to the top. These findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets. PageRank is one of the principle criteria according to which Google ranks Web pages. PageRank can be interpreted as a frequency of visiting a Web page by a random surfer, and thus it reflects the popularity of a Web page. Google computes the PageRank using the power iteration method, which requires about one week of intensive computations. In the present work we propose and analyze Monte Carlo-type methods for the PageRank computation. There are several advantages of the probabilistic Monte Carlo methods over the deterministic power iteration method: Monte Carlo methods already provide good estimation of the PageRank for relatively important pages after one iteration; Monte Carlo methods have natural parallel implementation; and finally, Monte Carlo methods allow one to perform continuous update of the PageRank as the structure of the Web changes. The PageRank algorithm, used in the Google search engine, greatly improves the results of Web search by taking into account the link structure of the Web. PageRank assigns to a page a score proportional to the number of times a random surfer would visit that page, if it surfed indefinitely from page to page, following all outlinks from a page with equal probability. We propose to improve PageRank by using a more intelligent surfer, one that is guided by a probabilistic model of the relevance of a page to a query. Efficient execution of our algorithm at query time is made possible by precomputing at crawl time (and thus once for all queries) the necessary terms. Experiments on two large subsets of the Web indicate that our algorithm significantly outperforms PageRank in the (human-rated) quality of the pages returned, while remaining efficient enough to be used in today's large search engines.