Personalized search

Personalized search refers to web search experiences that are tailored specifically to an individual's interests by incorporating information about the individual beyond specific query provided. Pitkow et al. describe two general approaches to personalizing search results, one involving modifying the user's query and the other re-ranking search results.

Google introduced personalized search in 2004 and it was implemented in 2005 to Google search. Google has personalized search implemented for all users, not only those with a Google account. There is not very much information on how exactly Google personalizes their searches; however, it is believed that they use user language, location, and web history.

Early search engines, like Google and AltaVista, found results based only on key words. Personalized search, as pioneered by Google, has become far more complex with the goal to "understand exactly what you mean and give you exactly what you want." Using mathematical algorithms, search engines are now able to return results based on the number of links to and from sites; the more links a site has, the higher it is placed on the page. Search engines have two degrees of expertise: the shallow expert and the deep expert. An expert from the shallowest degree serves as a witness who knows some specific information on a given event. A deep expert, on the other hand, has comprehensible knowledge that gives it the capacity to deliver unique information that is relevant to each individual inquirer. If a person knows what he or she wants than the search engine will act as a shallow expert and simply locate that information. But search engines are also capable of deep expertise in that they rank results indicating that those near the top are more relevant to a user's wants than those below.

While many search engines take advantage of information about people in general, or about specific groups of people, personalized search depends on a user profile that is unique to the individual. Research systems that personalize search results model their users in different ways. Some rely on users explicitly specifying their interests or on demographic/cognitive characteristics. However, user-supplied information can be difficult to collect and keep up to date. Others have built implicit user models based on content the user has read or their history of interaction with Web pages.

There are several publicly available systems for personalizing Web search results (e.g., Google Personalized Search and Bing's search result personalization). However, the technical details and evaluations of these commercial systems are proprietary. One technique Google uses to personalize searches for its users is to track log in time and if the user has enabled web history in his browser. If a user accesses the same site through a search result from Google many times, it believes that they like that page. So when users carry out certain searches, Google's personalized search algorithm gives the page a boost, moving it up through the ranks. Even if a user is signed out, Google may personalize their results because it keeps a 180-day record of what a particular web browser has searched for, linked to a cookie in that browser.

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