Randomly exploring the long tail of search results
I sometimes click on a random “deep” search result page to see if anything interesting turns up, because of the limitations of popularity and PageRank for some queries.
Paul Kedrosky points at a recent paper from CMU which suggests randomly mixing in some low ranking pages may improve search results over time.
Unfortunately, the correlation between popularity and quality
is very weak for newly-created pages that have few
visits and/or in-links. Worse, the process by which new,
high-quality pages accumulate popularity is actually inhibited
by search engines. Since search engines dole out
a limited number of clicks per unit time among a large
number of pages, always listing highly popular pages at
the top, and because users usually focus their attention on
the top few results, newly-created but high-quality
pages are “shut out.”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.
Link:
Shuffling a Stacked Deck: The Case for Partially
Randomized Ranking of Search Engine Results,
