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. There are two general approaches to personalizing search results, one involving modifying the user's query and the other re-ranking search results. 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. There are 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 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 then 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. In search engines on social networking platforms like Facebook or LinkedIn, personalization could be achieved by exploiting homophily between searchers and results. For example, in People search, searchers are often interested in people in the same social circles, industries or companies. In Job search, searchers are usually interested in jobs at similar companies, jobs at nearby locations and jobs requiring expertise similar to their own. In order to better understand how personalized search results are being presented to the users, a group of researchers at Northeastern University compared 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 degrees 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 a user searched for 'used car sales', Google may produce results of local car dealerships in their 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 a user performs a search and follow it with a subsequent search, the results of the second search is influenced by the first search. A noteworthy point 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. Several concerns have been brought up regarding personalized search. It decreases the likelihood of finding new information by biasing search results towards what the user has already found. It introduces potential privacy problems in which a user may not be aware that their search results are personalized for them, and wonder why the things that they are interested in have become so relevant. Such a problem has been coined as the 'filter bubble' by author Eli Pariser. He argues that people are letting major websites drive their destiny and make decisions based on the vast amount of data they've collected on individuals. This can isolate users in their own worlds or 'filter bubbles' where they only see information that they want to, such a consequence of 'The Friendly World Syndrome'. As a result, people are much less informed of problems in the developing world which can further widen the gap between the North (developed countries) and the South (developing countries).