Currently, search engines principally use "one size fits all" ranking algorithms to deliver homogeneous search results to searchers with heterogeneous search objectives.See also my March 2005 post, "The key challenge is personalization", where I said:
Personalized algorithms produce search results that are custom-tailored to each searcher's interests, so different searchers will see different results.
Personalized ranking algorithms represent the next major advance in search relevancy ... Improvements in one-size-fits-all algorithms will yield progressively smaller relevancy benefits. Personalized algorithms transcend those limits [by] optimizing relevancy for each searcher.
Personalized ranking algorithms also reduce the effects of search engine bias. Personalized algorithms mean that there are multiple "top" search results for a particular search term, instead of a single "winner," so web publishers won't compete against each other in a zero-sum game ... Also, personalized algorithms necessarily will diminish the weight given to popularity-based metrics (to give more weight for searcher-specific factors), reducing the structural biases due to popularity.
With only one generalized relevance rank, further improvements to search quality become increasingly difficult because people disagree on how relevant a particular page is to a particular search.See also my July 2006 post, "Combating web spam with personalization", where I said:
At some point, to get further improvements, relevance rank will have to be customized to each person's definition of relevance.
Another way to reduce the value [of web spam] is to reduce the maximum payoff. If different people see different search results, spamming becomes much less attractive. The jackpot from getting to the top of the page disappears.See also my August 2006 post, "Web spam, AIRWeb, and SIGIR", where I said:
Personalized search shows different search results to different people based on their history and their interests. Not only does this increase the relevance of the search results, but also it makes the search results harder to spam.
"Winner takes all" encourages spam. When spam succeeds in getting the top slot, everyone sees the spam. It is like winning the jackpot.
If different people saw different search results -- perhaps using personalization based on history to generate individualized relevance ranks -- this winner takes all effect should fade and the incentive to spam decline.