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Personalized search refers to 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. ==History== Google introduced Personalized search in 2004 and it was implemented in 2005 to Google search. Google has personalized search set up for not just those who have a Google account but everyone as well. 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.〔http://personalization.ccs.neu.edu/paper.pdf〕 Early search engines, like Yahoo! 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 an 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. But user supplied information can be hard 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. The more you keep going the same site through a search result from Google, it believes that you like that page. So when you do certain searches, Google's personalized search algorithm gives the page a boost, moving it up through the ranks. Even if you're signed out, Google may personalize your results because it keeps a 180-day record of what a particular web browser has searched for, linked to a cookie in that browser. In order to better understand how personalized search results are being presented to the users, a group of researchers at Northeastern University set out to answer this question. By comparing an aggregate set of searches from logged in users against a control group, the research team found that 11.7% of results show differences due to personalization, however this varies widely by search query and result ranking position. Of various factors tested, the two that had measurable impact were being logged in with a Google account and the IP address of the searching users. It should also be noted that results with high degress of personalization include companies and politics. One of the factors driving personalization is localization of results, with company queries showing store locations relevant to the location of the user. So, for example, if you searched for "used car sales", Google may churn out results of local car dealerships in your area. On the other hand, queries with the least amount of personalization include factual queries ("what is") and health. When measuring personalization, it is important to eliminate background noise. In this context, one type of background noise is the carry-over effect. The carry-over effect can be defined as follows: when you perform a search and follow it with a subsequent search, the results of the second search is influenced by the first search. An interesting point to note is that the top ranked URLs are less likely to change based off personalization, with most personalization occurring at the lower ranks. This is a style of personalization, based on recent search history, but it is not a consistent element of personalization because the phenomenon times out after 10 minutes, according to the researchers. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Personalized search」の詳細全文を読む スポンサード リンク
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