Google Personalized Search is a personalized search feature of Google Search. All searches on Google Search are associated with a browser cookie record. Then, when a user performs a search, the search results are not only based on the relevance of each web page to the search term, but also on which websites the user (or someone else using the same browser) visited through previous search results. This provides a more personalized experience that can increase the relevance of the search results for the particular user, but also has some side effects, such as informing other users of the same IP address or computer about what others have been searching for, or creating a filter bubble. The feature only takes effect after several searches have been recorded, so that it can be calibrated to the user's tastes.
Personalized Search was originally introduced on March 29, 2004 as a beta test of a Google Labs project. On April 20, 2005, it was made available as a non-beta service, but still separate from ordinary Google Search. On November 11, 2005, it became a part of the normal Google Search, but only to users with Google Accounts.
Beginning on December 4, 2009, Personalized Search was applied to all users of Google Search, including those who are not logged into a Google Account.
In addition to customizing results based on personal behavior and interests associated with a Google Account, Google also implemented social search results in October 2009 based on people whom one knows. 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". These two services integrated into regular results by February 2011 and expanded results by including content shared to users known through social networks.
Google's search algorithm is driven by collecting and storing web history in its databases. For non-authenticated users Google looks at anonymously stored browser cookies on a user's browser and compares the unique string with those stored within Google databases. Google accounts logged into Google Chrome use user's web history to learn what sites and content you like and base the search results presented on them. Using the data provided by the user Google constructs a profile including gender, age, languages, and interests based on prior site traffic.
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. These scores are then used along with many other things to determine if a page will rank well in a search." "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. In essence, Google interprets a link from page A to page B as a vote, by page A, for page B. But, Google looks at considerably more than the sheer volume of votes, or links a page receives; for example, it also analyzes the page that casts the vote. Votes cast by pages that are themselves "important" weigh more heavily and help to make other pages 'important.' Using these and other factors, Google provides its views on the pages' relative importance,"
The top factors in personalizing search results are:
Each of these variables will factor into the personalization of a user's search results in hopes of quickly providing the most relevant results to the user to answer whatever question is being asked.
Location data allows Google to provide information based upon current location and places that the user has visited in the past, based upon GPS location from an Android smartphone or the user's IP address. Google uses this location data to provide local listings grouped with search results using the Google Local platform featuring detailed reviews and ratings from Zagat.
Search history was first used to personalize search results in 2005 based upon previous searches and clicked links by individual end users . Then, in 2009, Google announced that personalized search would no longer require a user to be logged in, and instead Google would use an anonymous cookie in a web browser to customize search results for those who were not logged in.
Web history differs from search history, as it's a record of the actual pages that a user visits, but still provides contributing factors in ranking search results. 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.
Google's social networking service, Google+ also collects this demographic data including age, sex, location, career, and friends. This largely comes into play when presenting reviews and ratings from people within a user's circle.
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. The research showed that this result varies widely by search query and result ranking position.
Several concerns have been brought up regarding the feature. It decreases the likelihood of finding new information, since it biases search results towards what the user has already found. It also introduces some privacy problems, since a user may not be aware that their search results are personalized for them, and it affects the search results of other people who use the same computer (unless they are logged in as a different user). The feature also has profound effects on the search engine optimization (SEO) industry, since search results are not ranked the same way for every user - thus making it more difficult to identify the effects of SEO efforts. Personalization makes search experience inconsistent for different users requiring the SEO industry to be aware of both personalized and non-personalized search results to get an increase in ranking.
Personalized search suffers from creating an abundance of background noise to search results. This can be seen as the carry-over effect where one search is performed followed by a subsequent search. The second search is influenced by the first search if a timeout period is not set at a high enough threshold. An example of the negative effects of the carry-over effect is a search for a store in Hawaii could carry-over the results of a previous, failed search that showed the same store in California, creating noise.
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