Personalized search

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Personalized search is a

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."^[3] 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.^[3] 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.^[4] 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.^[4]

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.^[5][6] 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.^{[7][8][9][10][11]}

There are several publicly available systems for personalizing Web search results (e.g.,

In search engines on social networking platforms like Facebook or LinkedIn, personalization could be achieved by exploiting homophily between searchers and results.^[14] 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

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 on 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.^[15]

Several concerns have been brought up regarding personalized search. It decreases the likelihood of finding new information by

The methods of personalization, and how useful it is to "promote" certain results which have been showing up regularly in searches by like-minded individuals in the same community. The personalization method makes it very easy to understand how the filter bubble is created. As certain results are bumped up and viewed more by individuals, other results not favored by them are relegated to obscurity. As this happens on a community-wide level, it results in the community, consciously or not, sharing a skewed perspective of events.[17] Filter bubbles have become more frequent in search results and are envisaged as disruptions to information flow in online more specifically social media.^[18]

An area of particular concern to some parts of the world is the use of personalized search as a form of control over the people utilizing the search by only giving them particular information (

The feature also has profound effects on the search engine optimization industry, due to the fact that search results will no longer be ranked the same way for every user.^[21] An example of this is found in Eli Pariser's, The Filter Bubble, where he had two friends type in "BP" into Google's search bar. One friend found information on the BP oil spill in the Gulf of Mexico while the other retrieved investment information.^[16] The aspect of information overload is also prevalent when using search engine optimization. However, one means of managing information overload is through accessing value-added information—information that has been collected, processed, filtered, and personalized for each individual user in some way.^[22] For instance, Google uses various ‘‘signals’’ in order to personalize searches including location, previous search keywords and recently contacts in a user’s social network while on the other hand, Facebook registers the user’s interactions with other users, the so-called ‘‘social gestures’’.^[22] The social gestures in this case include things such as use likes, shares, subscribe and comments.  When the user interacts with the system by consuming a set of information, the system registers the user interaction and history. On a later date, on the basis of this interaction history, some critical information is filtered out. This include content produced by some friends might be hidden from the user. This is because the user did not interact with the excluded friends over a given time. It is also essential to note that within the social gestures, photos and videos receives higher ranking than regular status posts and other related posts.^[22]

The filter bubble has made a heavy effect on the search for information of health. With the influence of search results based upon search history, social network, personal preference and other aspects, misinformation has been a large contributor in the drop of vaccination rate. In 2014/15 there was an outbreak of measles in America with there being 644 reported cases during the time period. The key contributors to this outbreak were anti-vaccine organizations and public figures, who at the time were spreading fear about the vaccine.^[23]

Some have noted that personalized search results not only serve to customize a user's search results, but also

An important example of search personalization is

One example of Google's ability to personalize searches is in its use of Google News. Google has geared its news to show everyone a few similar articles that can be deemed interesting, but as soon as the user scrolls down, it can be seen that the news articles begin to differ. Google takes into account past searches as well as the location of the user to make sure that local news gets to them first. This can lead to a much easier search and less time going through all of the news to find the information one want. The concern, however, is that the very important information can be held back because it does not match the criteria that the program sets for the particular user. This can create the "filter bubble" as described earlier.^[16]

An interesting point about personalization that often gets overlooked is the privacy vs personalization battle. While the two do not have to be mutually exclusive, it is often the case that as one becomes more prominent, it compromises the other. Google provides a host of services to people, and many of these services do not require information to be collected about a person to be customizable. Since there is no threat of privacy invasion with these services, the balance has been tipped to favor personalization over privacy, even when it comes to search. As people reap the rewards of convenience from customizing their other Google services, they desire better search results, even if it comes at the expense of private information. Where to draw the line between the information versus search results tradeoff is new territory and Google gets to make that decision. Until people get the power to control the information that is being collected about them, Google is not truly protecting privacy.

Google can use multiple methods of personalization such as traditional, social, geographic, IP address, browser, cookies, time of day, year, behavioral, query history, bookmarks, and more. Although having Google personalize search results based on what users searched previously may have its benefits, there are negatives that come with it.^[25][26] With the power from this information, Google has chosen to enter other sectors it owned, such as videos, document sharing, shopping, maps, and many more. Google has done this by steering searchers to their own services offered as opposed to others such as MapQuest.

Using search personalization, Google has doubled its video market share to about eighty percent. The legal definition of a monopoly is when a firm gains control of seventy to eighty percent of the market. Google has reinforced this monopoly by creating significant barriers of entry such as manipulating search results to show their own services. This can be clearly seen with Google Maps being the first thing displayed in most searches.

The analytical firm Experian Hitwise stated that since 2007, MapQuest has had its traffic cut in half because of this. Other statistics from around the same time include Photobucket going from twenty percent of market share to only three percent, Myspace going from twelve percent market share to less than one percent, and ESPN from eight percent to four percent market share. In terms of images, Photobucket went from 31% in 2007 to 10% in 2010 and Yahoo Images has gone from 12% to 7%.^[27] It becomes apparent that the decline of these companies has come because of Google's increase in market share from 43% in 2007 to about 55% in 2009.^[27]

There are two common themes with all of these graphs. The first is that Google's market share has a direct inverse relationship to the market share of the leading competitors. The second is that this directly inverse relationship began around 2007, which is around the time that Google began to use its "Universal Search" method.^[28]

Two studies examined the effects of personalized screening and ordering tools, and the results show a

The first study was conducted by Kristin Diehl from the University of South Carolina. Her research discovered that reducing search cost led to lower quality choices. The reason behind this discovery was that 'consumers make worse choices because lower search costs cause them to consider inferior options.' It also showed that if consumers have a specific goal in mind, they would further their search, resulting in an even worse decision.^[29] The study by Gerald Haubl from the University of Alberta and Benedict G.C. Dellaert from Maastricht University mainly focused on recommendation systems. Both studies concluded that a personalized search and recommendation system significantly improved consumers' decision quality and reduced the number of products inspected.^[29]

On the same note the use of the use of filter bubbles in personalized search has also led to several benefits to the users. For instance filter bubbles have the potential of enhancing opinion diversity by allowing like-minded citizens to come together and reinforce their beliefs. This also helps in protecting users from fake and extremist content by enclosing them in bubbles of reliable and verifiable information.^[30] Filter bubbles can be an important element of information freedom by providing users more choice.^[30]

Personalized search has also proved to work on the benefit of the user in the sense that they improve the information search results. Personalized search tailors search result to the needs of the user in the sense that it matches what the user wants with past search history.^[31] This also helps reduce the amount of irrelevant information and also reduces the amount of time users spend in searching for information. For instance, in Google, the search history of user is kept and matched with the user query in the user's next searches. Google achieves this through three important techniques. The three techniques include (i) query reformulation using extra knowledge, i.e., expansion or refinement of a query, (ii) post filtering or re-ranking of the retrieved documents (based on the user profile or the context), and (iii) improvement of the IR model.^[31]

Personalized search can improve search quality significantly and there are mainly two ways to achieve this goal:

The first model available is based on the users' historical searches and search locations. People are probably familiar with this model since they often find the results reflecting their current location and previous searches.

There is another way to personalize search results. In Bracha Shapira and Boaz Zabar's "Personalized Search: Integrating Collaboration and Social Networks", Shapira and Zabar focused on a model that utilizes a

Recent paper “Search personalization with embeddings” shows that a new embedding model for search personalization, where users are embedded on a topical interest space, produces better search results than strong learning-to-rank models.

The foundation of the argument against the use of personalized search is because it limits the users' ability to become exposed to material that would be relevant to the user's search query but due to the fact that some of this material differs from the user's interests and history, the material is not displayed to the user. Search personalization takes the objectivity out of the search engine and undermines the engine. "Objectivity matters little when you know what you are looking for, but its lack is problematic when you do not".^[33] Another criticism of search personalization is that it limits a core function of the web: the collection and sharing of information. Search personalization prevents users from easily accessing all the possible information that is available for a specific search query. Search personalization adds a bias to user's search queries. If a user has a particular set of interests or internet history and uses the web to research a controversial issue, the user's search results will reflect that. The user may not be shown both sides of the issue and miss potentially important information if the user's interests lean to one side or another. A study done on search personalization and its effects on search results in Google News resulted in different orders of news stories being generated by different users, even though each user entered the same search query. According to Bates, "only 12% of the searchers had the same three stories in the same order. This to me is prima facie evidence that there is filtering going on".^[34] If search personalization was not active, all the results in theory should have been the same stories in an identical order.

Another disadvantage of search personalization is that internet companies such as Google are gathering and potentially selling their users' internet interests and histories to other companies. This raises a privacy issue concerning whether people are comfortable with companies gathering and selling their internet information without their consent or knowledge. Many web users are unaware of the use of search personalization and even fewer have knowledge that user data is a valuable commodity for internet companies.

E. Pariser, author of The Filter Bubble, explains how there are differences that search personalization has on both