Category Archives: Search Engine Optimization (SEO)

Search Engine Optimization tips and strategies and information, from SEO by the Sea, to help make web sites easier to find.

Learning SEO by Looking at Anomalies

Sometimes one of the best ways to learn is to question why you see something that you possibly shouldn’t be seeing. This can produce more interesting lessons than even digging into things like patents and whitepapers. For instance, I published a post last night, October 4th, 2016, on Context Vectors. On Twitter this morning, Dan Shure had a question about the snippet for my post, wondering why the snippet date was April 10, 2016. I knew the answer immediately.

Dan’s Questioning Tweet:

Question about Snippet Date

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Google Patents Context Vectors to Improve Search

Horse from Virginia Farm
For example, a horse to a rancher is an animal. A horse to a carpenter is an implement of work. A horse to a gymnast is an implement on which to perform certain exercises.

One of the limitations of information on the Web is that it is organized differently at each site on the Web. As a newly granted Google patent notes, there is no official catalog of information available on the internet, and each site has its own organizational system. Search engines exist to index information, but they have issues, as described in this new patent that make finding information challenging.

Limitations on Conventional Keyword-Based Search Engines

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Twitter Poll – How Does Google Index Content on the Web?

Google Indexes by Websites, Pages, or URLs

I thought this was an interesting question to ask people because I think it’s often misunderstood. Google treats content found at different URLs as if it is different content, even though it might be the same, such as in the following examples:

One of the most interesting papers I’ve come across on this topic is this one (One of the authors joined Google shortly after this was released – Ziv Bar-Yossef):

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Machine Learning Inside Google

By OSX - Own work, Public Domain,
By OSX – Own work, Public Domain,

Understanding Systems

When I was in high school, one of the required classes I had to take was a shop class. I had been taking mostly what the school called “enriched” courses, or what were mostly academic classes that featured primarily reading, writing, and arithmetic. A shop class had more of a trade focus. I was surprised when the first lesson on the first day of my shop class was a richer academic experience than any of the enriched classes I had taken.

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How Google May Map a Query to an Entity for Suggestions

Search predictions come from:

– The terms you’re typing.

– What other people are searching for, including trending searches. Trending searches are popular stories in your area that change throughout the day. Trending searches aren’t related to your search history.

– Relevant searches you’ve done in the past (if you’re signed in to your Google Account and have Web & App Activity turned on).

Note: Search predictions aren’t the answer to your search, and they’re not statements by other people or Google about your search terms.

~ Search on Google using autocomplete

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Google’s Reasonable Surfer Patent Updated


Systems and methods consistent with the principles of the invention may provide a reasonable surfer model that indicates that when a surfer accesses a document with a set of links, the surfer will follow some of the links with higher probability than others. This reasonable surfer model reflects the fact that not all of the links associated with a document are equally likely to be followed. Examples of unlikely followed links may include “Terms of Service” links, banner advertisements, and links unrelated to the document.

Google’s original PageRank algorithm is based upon what its inventor referred to as the Random Surfer model, where it ranked pages on the Web based upon a probability that a person following links at random on the Web might end up upon a particular page:

The rank of a page can be interpreted as the probability that a surfer will be at the page after following a large number of forward links. The constant α in the formula is interpreted as the probability that the web surfer will jump randomly to any web page instead of following a forward link.

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Satisfaction a Future Ranking Signal in Google Search Results?

Do you search through Google on your phone? How do you know whether or not Google is watching you as you do and keeps on eye on whether or not you like the results you receive during your searches? Could Satisfaction with search results be a ranking signal that Google may use now, or in the future?

A newly published Google patent application describes technology that would modify scoring and ranking of query results using biometric indicators of user satisfaction or negative engagement with a search result. In other words; Google would track how satisfied or unsatisfied someone might be with search results, and using machine learning, build a model based upon that satisfaction, raising or lowering search results for a query. This kind of reaction might be captured using a camera on a searcher’s phone to see their reaction to a search result, as depicted in the following screenshot from the patent:

Google biometric responses

This satisfaction would be based upon Google tracking and measuring biometric parameters of a user obtained after thst search result is presented to the user, to determine whether those may indicate negative engagement by the user with a search result.

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Google Cross Device Tracking and Audio Watermarks

Historical recording Aboriginal Corroboree,  ,a href="">Australian Broadcasting Corporation, Some rights reserved
Historical recording Aboriginal Corroboree, Australian Broadcasting Corporation, Some rights reserved

Advertising on the Web is going through some changes because of how smart phones and tablets track visitors on a site, and how advertisements may broadcast high-frequency sounds that may act as audio watermarks that other devices can pick up upon. Imagine watching TV, and your TV broadcasts a high-frequency sound from an advertisement that your phone hears, and shares with the advertiser, who may then track whether you search for or purchase the product offered on a web site?

These are well described in the following Irish Examiner article, Future of Mobile: Advertisers and the quest for your data. If you read that and have some familiarity with how Google works, you may ask yourself if Google has followed such practices, or shown any sign of doing so.

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