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In December 2009, Google announced it would be using the web search history of all its users in order to populate search results.<ref>{{cite web|url=https://googleblog.blogspot.com/2009/12/personalized-search-for-everyone.html|title=Personalized Search for everyone|access-date=December 14, 2009|archive-date=December 8, 2009|archive-url=https://web.archive.org/web/20091208140917/http://googleblog.blogspot.com/2009/12/personalized-search-for-everyone.html|url-status=live}}</ref> On June 8, 2010 a new web indexing system called [[Google Caffeine]] was announced. Designed to allow users to find news results, forum posts, and other content much sooner after publishing than before, Google Caffeine was a change to the way Google updated its index in order to make things show up quicker on Google than before. According to Carrie Grimes, the software engineer who announced Caffeine for Google, "Caffeine provides 50 percent fresher results for web searches than our last index..."<ref>{{cite web |url=http://googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html |title=Our new search index: Caffeine |publisher=Google: Official Blog |access-date=May 10, 2014 |archive-date=June 18, 2010 |archive-url=https://web.archive.org/web/20100618160021/http://googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html |url-status=live }}</ref> [[Google Instant]], real-time-search, was introduced in late 2010 in an attempt to make search results more timely and relevant. Historically site administrators have spent months or even years optimizing a website to increase search rankings. With the growth in popularity of social media sites and blogs, the leading engines made changes to their algorithms to allow fresh content to rank quickly within the search results.<ref>{{cite web |title=Relevance Meets Real-Time Web |publisher=[[Google Blog]] |url=http://googleblog.blogspot.com/2009/12/relevance-meets-real-time-web.html |access-date=January 4, 2010 |archive-date=April 7, 2019 |archive-url=https://web.archive.org/web/20190407221454/http://googleblog.blogspot.com/2009/12/relevance-meets-real-time-web.html |url-status=live }}</ref>
In February 2011, Google announced the [[Google Panda|Panda]] update, which penalizes websites containing content duplicated from other websites and sources. Historically websites have copied content from one another and benefited in search engine rankings by engaging in this practice. However, Google implemented a new system that punishes sites whose content is not unique.<ref>{{cite web|title=Google Search Quality Updates|publisher=[[Google Blog]]|url=http://googleblog.blogspot.com/2011/02/finding-more-high-quality-sites-in.html|access-date=March 21, 2012|archive-date=April 23, 2022|archive-url=https://web.archive.org/web/20220423234246/https://googleblog.blogspot.com/2011/02/finding-more-high-quality-sites-in.html|url-status=live}}</ref> The 2012 [[Google Penguin]] attempted to penalize websites that used manipulative techniques to improve their rankings on the search engine.<ref>{{cite web|title=What You Need to Know About Google's Penguin Update|work=Inc |date=June 20, 2012|publisher=[[Inc.com]]|url=http://www.inc.com/aaron-aders/what-you-need-to-know-about-googles-penguin-update.html|access-date=December 6, 2012|archive-date=December 20, 2012|archive-url=https://web.archive.org/web/20121220235821/http://www.inc.com/aaron-aders/what-you-need-to-know-about-googles-penguin-update.html|url-status=live |last1=Aders |first1=Aaron }}</ref> Although Google Penguin has been presented as an algorithm aimed at fighting web spam, it really focuses on spammy links<ref>{{Cite news|url=http://searchengineland.com/google-penguin-looks-mostly-link-source-says-google-260902|title=Google Penguin looks mostly at your link source, says Google|date=2016-10-10|work=Search Engine Land|access-date=2017-04-20|language=en-US|archive-date=April 21, 2017|archive-url=https://web.archive.org/web/20170421001835/http://searchengineland.com/google-penguin-looks-mostly-link-source-says-google-260902|url-status=live}}</ref> by gauging the quality of the sites the links are coming from. The 2013 [[Google Hummingbird]] update featured an algorithm change designed to improve Google's natural language processing and semantic understanding of web pages. Hummingbird's language processing system falls under the newly recognized term of "conversational search", where the system pays more attention to each word in the query in order to better match the pages to the meaning of the query rather than a few words.<ref>{{cite web|title=FAQ: All About The New Google "Hummingbird" Algorithm|url=https://searchengineland.com/google-hummingbird-172816|website=www.searchengineland.com|date=September 26, 2013|access-date=17 March 2018|archive-date=December 23, 2018|archive-url=https://web.archive.org/web/20181223110045/https://searchengineland.com/google-hummingbird-172816|url-status=live}}</ref> With regards to the changes made to search engine optimization, for content publishers and writers, Hummingbird is intended to resolve issues by getting rid of irrelevant content and spam, allowing Google to produce high-quality content and rely on them to be 'trusted' authors.
In October 2019, Google announced they would start applying [[BERT (language model)|BERT]] models for English language search queries in the US. Bidirectional Encoder Representations from Transformers (BERT) was another attempt by Google to improve their natural language processing, but this time in order to better understand the search queries of their users.<ref>{{Cite web|title=Understanding searches better than ever before|url=https://blog.google/products/search/search-language-understanding-bert/|date=2019-10-25|website=Google|language=en|access-date=2020-05-12|archive-date=January 27, 2021|archive-url=https://web.archive.org/web/20210127042834/https://www.blog.google/products/search/search-language-understanding-bert/|url-status=live}}</ref> In terms of search engine optimization, BERT intended to connect users more easily to relevant content and increase the quality of traffic coming to websites that are ranking in the [[Search engine results page|Search Engine Results Page]].
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