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Are you sitting down? This statistic may shock you: Google uses neural matching instead of traditional SERP ranking factors for 30% of queries!
Last few months, Google has been using neural matching, –AI method to better connect words to concepts. Super synonyms, in a way, and impacting 30% of queries. Don’t know what “soap opera effect” is to search for it? We can better figure it out. pic.twitter.com/Qrwp5hKFNz
— Danny Sullivan (@dannysullivan) September 24, 2018
Google’s Danny Sullivan describes neural matching as an “AI method to better connect words to concepts,” noting in a later tweet that “how people search is often different from information that people write solutions about.”
In other words, a single word may have multiple meanings, and Google must identify the intent of the search beyond the surface query. They want to bridge intent with content, to use “super synonyms” to improve relevance of results.
This is a look back at a big change in search but which continues to be important: understanding synonyms. How people search is often different from information that people write solutions about. pic.twitter.com/sBcR4tR4eT
— Danny Sullivan (@dannysullivan) September 24, 2018
To work around this vast challenge, Google’s AI has increasingly leveraged Document Relevance Ranking, known as Ad-hoc Retrieval, which ranks pages based on the relevance between the query and the text content only. This new ranking paradigm is unique, in contrast with traditional ranking formulas which include content, network structure and link signals.
In recently published research (Deep Relevance Ranking using Enhanced Document-Query Interactions), Google describes using traditional ranking signals first (to limit the scope of all returned results), then applying Ad-hoc Retrieval to determine the most relevant (and thus, the top ten) search results.
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