Google's Search Team describes how the placement of news, images, and videos come into play

Gary Illyes, the Trends Analyst at Google explained how the universal engine at Google works in the latest podcast. The positioning of all the key features surprisingly gets decided through an auction.

As per Gary, each feature submits a bid of a specific place in the search result, the one they desire. Google then decides its position. This is primarily decided through the platform's overall search engine, the 'super engine' as they say. The placements aren't decided randomly however, they are based on a number of factors, however Gary did not shine much light on the matter.

He did explain however that each genre has its mini search engine which ranks the results accordingly. They are then mixed up by the main search engine and finally displayed on the main page that users come in contact with. For example, videos would be ranked accordingly and images in context to their search engine which would then be displayed on the main page by the universal engine based on several factors.

Gary further went on to inform users that Google scores every result in each index. However it is done by assigning scores to each item and then assigning those items in each index. For example the news results will be scored individually on the new index while the video results will be scored each on the video index. The indexes then bid in an auction for their desired position. If an index doesn't want the third or fourth position, they can request for that as well, however it will be an active part of the bid.

Gary told users that oftentimes people get confused in a concept hence he sees it as his responsibility to clear it up. It is essential to note that every item isn't subjected to Google for a rank but only genres or features are. Hence Google always auctions the position of the index and not the content.

Lastly, he pondered over how Google decides which index is most relevant in each result. It does so through observed learning. The learning pattern follows a strict observance of which results are more clicked on by users for example when searching for wild flowers, if the images section is more clicked on than videos, it will be positioned first however if searching for rainy weather, the news index is frequently opened, it will be the first option.

To conclude, it is the users that teach Google which indexes to place first. The incentive behind the podcast is to inform users more about how their actions affect results and to create maximum transparency.

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