In-degree, PageRank, number of visits and other measures of Web page
popularity significantly influence the ranking of search results by modern
search engines. The assumption is that popularity is closely correlated with
quality, a more elusive concept that is difficult to measure directly.
Unfortunately, the correlation between popularity and quality is very weak for
newly-created pages that have yet to receive many visits and/or in-links.
Worse, since discovery of new content is largely done by querying search
engines, and because users usually focus their attention on the top few
results, newly-created but high-quality pages are effectively ``shut out,'' and
it can take a very long time before they become popular.
We propose a simple and elegant solution to this problem: the introduction of
a controlled amount of randomness into search result ranking methods. Doing so
offers new pages a chance to prove their worth, although clearly using too much
randomness will degrade result quality and annul any benefits achieved. Hence
there is a tradeoff between exploration to estimate the quality of new pages
and exploitation of pages already known to be of high quality. We study this
tradeoff both analytically and via simulation, in the context of an economic
objective function based on aggregate result quality amortized over time. We
show that a modest amount of randomness leads to improved search results
