Five Years of Google Ranking Signals

Organic Search Google Ranking Signals

1. Domain Age and Rate of Linking
2. Use of Keywords
3. Related Phrases
4. Keywords in Main Headings, Lists, and Titles
5. Page Speed
6. Watch Times for a Page
7. Context Terms on a Page
8. Language Models Using Ngrams
9. Gibberish Content
10. Authoritative Results
11. How Well Databases Answers Match Queries
12. Suspicious Activity to Increase Rankings
13. Popularity Scores for Events
14. The Amount of Weight from a Link is Based upon the Probability that someone might click upon it
15. Biometric Parameters while Viewing Results
16. Click-Throughs
17. Site Quality Scores
18. Disambiguating People
19. Effectiveness and Affinity
20. Quotes
21. Category Duration Visits
22. Repeat Clicks and Visit Durations
23. Environmental Information
24. Traffic Producing Links
25. Freshness
26. Media Consumption History
27. Geographic Coordinates
28. Low Quality
29. Television Viewing
30. Quality Rankings

Semantic Search Google Ranking Signals

31. Searches using Structured Data
32. Related Entities
33. Nearby Locations
34. Attributes of Entities
35. Natural Language Search Results

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Google to Offer Combined Content (Paid and Organic) Search Results

Combined Content Search Results

Google Introduces Combined Content Results

This new patent is about “Combined content. What does that mean exactly? When Google patents talk about paid search, they refer to those paid results as “content” rather than as advertisements. This patent is about how Google might combine paid search results with organic results in certain instances.

The recent patent from Google (Combining Content with Search Results) tells us about how Google might identify when organic search results might be about specific entities, such as brands. It may also recognize when paid results are about the same brands, whether they might be products from those brands.

In the event that a set of search results contains high ranking organic results from a specific brand, and a paid search result from that same brand, the process described in the patent might allow for the creation of a combined content result of the organic result with the paid result.

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PageRank Update

Added: July 16, 2019 A Google Search Engineer on a thread at Hacker News told the world that Google stopped using the Stanford Version of PageRank back in 2006, which Barry Schwartz reported upon at Search Engine Roundtable in the post Former Google Engineer: Google Hasn’t Used PageRank Since 2006 That search engineer was Jonathan Tang, who has been an inventor on at least one Google Patent in the past. Tang stated in a longer post the following:

The comments here that PageRank is Google’s secret sauce also aren’t really true – Google hasn’t used PageRank since 2006. The ones about the search & clickthrough data being important are closer, but I suspect that if you made those public you still wouldn’t have an effective Google competitor.

He told us this about the change away from that version of PageRank:

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Topical Search Results at Google?

The Oldest Pepper Tree in California

At one point in time, search engines such as Google learned about topics on the Web from sources such as Yahoo! and the Open Directory Project, which provided categories of sites, within directories that people could skim through to find something that they might be interested in.

Those listings of categories included hierarchical topics and subtopics; but they were managed by human beings and both directories have closed down.

In addition to learning about categories and topics from such places, search engines used to use such sources to do focused crawls of the web, to make sure that they were indexing as wide a range of topics as they could.

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Using Ngram Phrase Models to Generate Site Quality Scores

Scrabble-phrases
Source: https://commons.wikimedia.org/wiki/File:Scrabble_game_in_progress.jpg
Photographer: McGeddon
Creative Commons License: Attribution 2.0 Generic

Navneet Panda, whom the Google Panda update is named after, has co-invented a new patent that focuses on site quality scores. It’s worth studying to understand how it determines the quality of sites.

Back in 2013, I wrote the post Google Scoring Gibberish Content to Demote Pages in Rankings, about Google using ngrams from sites and building language models from them to determine if those sites were filled with gibberish, or spammy content. I was reminded of that post when I read this patent.

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