Personalized search

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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.[1]

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."[2]Using mathematical algorithms, search engines are now able to return results based on the the number of links to an 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 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. [5]

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.[6][7] 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.[8][9][10][11][12]

There are several publicly available systems for personalizing Web search results (e.g., Google Personalized Search and Bing's search result personalization[13]). However, the technical details and evaluations of these commercial systems are proprietary.

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).[14]

Many search engines use concept-based user profiling strategies that derive only topics that users are highly interested in but for best results, according to researchers Wai-Tin and Dik Lun, both positive and negative preferences should be considered. Such profiles, applying negative and positive preferences, result in highest quality and most relevant results by separating alike queries from unalike queries. For example, typing in 'apple' could refer to either the fruit or the Macintosh computer and providing both preferences aids search engines' ability to learn which apple the user is really looking for based the links clicked. One concept-strategy the researchers came up with to improve personalized search and yield both positive and negative preferences is the click-based method. This method captures a user's interests based on which links they click on in a results list, while downgrading unclicked links. [15]

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.[16] 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. [17]

Some have noted that personalized search results not only serve to customize a user's search results, but also advertisements. This has been criticized as an invasion on privacy.[18]


  1. ^ Pitokow, James; Hinrich Schütze, Todd Cass, Rob Cooley, Don Turnbull, Andy Edmonds, Eytan Adar, Thomas Breuel (2002). "Personalized search". Communications of the ACM (CACM) 45 (9): 50–55. 
  2. ^ Remerowski, Ted (2013), National Geographic: Inside Google 
  3. ^ Remerowski, Ted (2013). National Geographic: Inside Google. 
  4. ^ Simpson, Thomas (2012). "Evaluating Google as an epistemic tool". Metaphilosophy 43 (4): 969–982. 
  5. ^ Simpson, Thomas (2012). "Evaluating Google as an epistemic tool". Metaphilosophy 43 (4): 969–982. 
  6. ^ Ma, Z.; Pant, G., and Sheng, O. (2007). "Interest-based personalized search.". ACM TOIS 25 (5). 
  7. ^ Frias-Martinez, E.; Chen, S.Y., and Liu, X. (2007). "Automatic cognitive style identification of digital library users for personalization.". JASIST 58 (2): 237–251. 
  8. ^ Chirita, P.; Firan, C., and Nejdl, W. (2006). "Summarizing local context to personalize global Web search". SIGIR: 287–296. 
  9. ^ Dou, Z.; Song, R., and Wen, J.R. (2007). "A large-scale evaluation and analysis of personalized search strategies". WWW: 581–590. 
  10. ^ Shen, X.; Tan, B. and Zhai, C.X. (2005). "Implicit user modeling for personalized search". CIKM: 824–831. 
  11. ^ Sugiyama, K.; Hatano, K., and Yoshikawa, M. (2004). "Adaptive web search based on user profile constructed without any effort from the user". WWW: 675–684. 
  12. ^ Teevan, J.; Dumais, S.T., and Horvitz, E. (2005). "Personalizing search via automated analysis of interests and activities". SIGIR: 415–422. 
  13. ^ Crook, Aidan, and Sanaz Ahari. "Making search yours". Bing. Retrieved 14 March 2011. 
  14. ^ Pariser, Eli (2011). The Filter Bubble. 
  15. ^ Wai-Tin, Kenneth; Dik Lun, L (2010). "Deriving concept-based user profiles from search engine logs". IEE transaction on knowledge and data engineering 22 (7): 969–982. 
  16. ^ "Google Personalized Results Could Be Bad for Search". Network World. Retrieved July 12, 2010.
  17. ^ Pariser, Eli (2011). The Filter Bubble. 
  18. ^ "Search Engines and Customized Results Based on Your Internet History". SEO Optimizers. Retrieved 27 February 2013.