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“Furthermore, such influences may be evanescent; for example, for an emergent subject, an information retrieval score may be more influential for the first several weeks, and then, at a later time, authority scores and user feedback scores may tend to grow in influence. Thus, tuning a search operation to compensate for these features is difficult prior to their detection, if not impossible.”

So, the focus of this patent is on “when certain features exhibit greater or lesser impacts on the ranking of resources for a search operation for a query and then adjust a search operation based on the impacts.”

If you’ve ever ranked a page in a fairly new subject area, and one day the search results that it appears in all of a sudden seem to shift around and change (undergoing a sea change), the next paragraph from the patent could explain why that might happen as search results get adjusted:

“The adjusted search operation is the re-run on the identified resources to re-rank the resources in a manner that takes into account the detected impacts. In some implementations, an initial search for a query is executed, and a proper subset of the ranked resources, e.g., the top N ranked resources, is processed to determine appropriate modifications to the search operation. The search operation, adjusted by the appropriate modifications, is then re-run to re-score and re-rank the resources.”

When I read the next paragraph in the patent, I was reminded of a post that Jason Barnard wrote about ranking at Google, based upon information he had received from Gary Illyes, Webmaster Trends Analyst at Google, which he wrote about in How Google Search Ranking Works – Darwinism in Search:

“The search engine utilizes a search operation that generates search scores for the resources and ranks the resources based on search scores. The search operation quantifies the relevance of the resources to the query, and the quantification can be based on a variety of factors. Such factors include information retrieval (“IR”) scores, user feedback scores, and optionally a separate ranking of each resource relative to other resources (e.g., an authority score). The search results are ordered in a first-order according to these search scores and provided to the user device according to the first order, or, in some situations, may be re-ranked by an adjusted search operation and provided to the user device as search results’ ranked according to a second-order that is different from the first order.”

This patent also tells us about feedback scores based upon information from query logs and click logs:

“In some implementations, the queries submitted from user devices are stored in query logs. Click data for the queries and the web pages referenced by the search results are stored in click logs. The query logs and the click logs define search history data that include data from and related to previous search requests. The query logs and click logs can be used to map queries submitted by the user devices to web pages that were identified in search results and the actions taken by users. The click logs and query logs can thus be used by the search system to determine queries submitted by the user devices, the actions taken in response to the queries, and how often the queries are submitted. Such information can be stored as feedback scores for the queries and resources.”

And Then There Is Reranking of Results, or Adjusted Search Features

This is part of an adjustment of results as has been described in the patent when there may be shifts in the values that results were scored upon to modify search results:

With that said, if the goal is to have a web page be displayed as a rich result in Google, then following the company’s official requirements is highly recommended.

Though it’s important to keep in mind that utilizing structured data does not guarantee that a web page will be displayed as a rich result, it simply makes it eligible to be displayed as a rich result.

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Using structured data types outside of what Google officially supports is optional, but also acceptable. Even if the structured data is not supported in the form of rich results, it still helps Google understand the content better and rank it accordingly.

Here’s how Mueller explains it:

“Independently, you’re always welcome to use structured data to provide better machine readable context for your pages. Which may not always result in visible changes, but can still help our systems to show your pages for relevant queries.”
Microsoft Ads Makes it Easier to Change a Campaign’s Audience Targeting
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Microsoft Advertising is rolling out campaign-level associations which will make easier to apply audience targeting.

Previously, audience targeting could only be applied at the ad group level.

If a campaign had several ad groups then the targeting would have to be changed on each one individually.

Now, audience targeting can be applied at the campaign level,  which means it will be applied to all ad groups in the campaign.

To get started with campaign-level associations, follow the steps below:

On the Campaigns page, click the Audiences tab.
Click Create association.
To use campaign-level associations, select Campaign.
Select the audiences you want to associate, set targeting setting and add bid adjustments.
Click Save to finish setup.
Microsoft Ads Makes it Easier to Change a Campaign’s Audience Targeting

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