Personalized Search was originally introduced on March 29, 2004 as a beta test of a Google Labs project.
Operating on the assumption that one's associates share similar interests, these results would give a ranking boost to sites from within a user's "Social Circle".
[7] Google's search algorithm is driven by collecting and storing web history in its databases.
[8] When a user performs a search using Google, the keywords or terms are used to generate ranked results based upon the PageRank algorithm.
This algorithm, according to Google, is their "system of counting link votes and determining which pages are most important based upon them.
"PageRank relies on the uniquely democratic nature of the web by using its vast link structure as an indicator of an individual page's value.
Lastly, Google+ data is used in search results as Google is provided a lot of demographics about a user from this information, such as age, gender, location, work history, interests, and social connections.
[13] Google's social networking service, Google+ also collects this demographic data including age, sex, location, career, and friends.
In order to determine the actual impacts of search customization on end users, researchers at Northeastern University determined in a study with logged in users vs. a control group that 11.7% of results show differences due to personalization.
It decreases the likelihood of finding new information, since it biases search results towards what the user has already found.
[15] However, in recent years new research had stated that search engines do not create the kind of filter bubbles previously thought.
The researchers note that filter bubbles sound like a real problem and that they primarily appear to apply to people other than yourself.