Search Engines/Filters/Personalized Search/Google/Pariser: Google developed a set of personalization algorithms that divide searchers into groups. A number of these were patented until 2008. Google gives an example in its patent application: People who collect shark teeth and those who do not get different selections of search results when they search for "big white incisors". (1)
Facebook/EdgeRank/Pariser: Facebook does it similarly. Even if you only have 100 friends, you receive too much stuff to read everything. Facebooks solution was EdgeRank that registered every interaction of users. The algorithm is complicated, the idea is based on three factors (2).
1. Degree of connectedness: how much time is spent on interacting with a particular person
2. Content weighting: which news items are selected?
3. Timeliness: more attention will be paid to messages posted recently.
1. Patentvolltext, aufgerufen am 10.12. 2010, http.//patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=1&p=1&f=G&1=50&d=PTXT&S1=7,451,130.PN.&OS=pn/7,451,130&RS=PN/7,451,13,
2. Jason Kincaid, »EdgeRank: The Secret Sauce That Makes Facebook’s News Feed Tick«, TechCrunch-Blog, 22. 04. 2010, aufgerufen am 10.12. 2010, http://techcrunch.com/2010/04/22/facebook-edgerank._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. The note [Author1]Vs[Author2] or [Author]Vs[term] is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition.
The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think London 2012