Typically, the introduction of machine-learning algorithm, was first introduced by Microsoft Bing in 2005, as RankNet; meanwhile, that same technology was later announced in 2015 by Google as RankBrain.
Also, in that order, is the introduction of Natural Language to Search, which was pioneered by Bing, and later introduced by Google.
The first technologies, RankNet and RankBrain, both process search results and provide more relevant search results for users by intelligent guessing what words or phrases might have a similar meaning and filter the result accordingly, making it more effective at handling never-before-seen search queries. While the second instance is applicable to booking flight online, it eliminates the need to manually select airport, flight destination, and other options in conducting a flight search, making the process automatic.
Now, local search is an interesting area where the first technology must come to bear to be truly relevant, and we conducted a minor comparison search with the long-tail keyword for "how to transfer a domain name to google domains in Nigeria", while Google boasting of both "Penguin" and "Panda" algorithm filtering, with the later claiming to filter scrapped contents.
The above image is actually a saved screen for Google result for the search query, which despite the glorified and hyped "Panda" algo still manages to place a scrapped forum post referencing an original article on this blog for that same keyword. Meanwhile,it is pertinent to note the local relevance of that search query and the enforcing "Nigeria" keyword implied. Find below the Bing.com returned result:
Now, comparing the earlier result from Google to the later from Bing.com, which do you think really did the job? Which search engine eliminated scrap content and provided an original locally relevant result for the search query. In as much as this may pass as an amateur comparison, a bigger picture won't be far from this established fact.
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