Google Giving Less Weight to Reviews of Places You Stop Visiting?

Google Timeline Reviews

The Weight of Google Reviews

I don’t consider myself paranoid, but after reading a lot of Google patents, I’ve been thinking of my phone as my Android tracking device. It’s looking like Google thinks of phones similarly; paying a lot of attention to things such as a person’s location history. After reading a recent patent, I’m fine with Google continuing to look at my location history, and reviews that I might write, even though there may not be any financial benefit to me. When I write a review of a business at Google, it’s normally because I’ve either really liked that place or disliked it, and wanted to share my thoughts about it with others.

A Google patent application filed and published by the search engine, but not yet granted is about reviews of businesses.

It tells us about how Google reviews can benefit businesses:

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Semantic Keyword Research and Topic Models

Seeing Meaning

I went to the Pubcon 2017 Conference this week in Las Vegas Nevada and gave a presentation about Semantic Search topics based upon white papers and patents from Google. My focus was on things such as Context Vectors and Phrase-Based Indexing.

I promised in social media that I would post the presentation on my blog so that I could answer questions if anyone had any.

I’ve been doing Semantic keyword research like this for years, where I’ve looked at other pages that rank well for keyword terms that I want to use, and identify phrases and terms that tend to appear upon those pages, and include them on pages that I am trying to optimize. It made a lot of sense to start doing that after reading about phrase based indexing in 2005 and later.

Some of the terms I see when I search for Semantic Keyword Research include such things as “improve your rankings,” and “conducting keyword research” and “smarter content.” I’m seeing phrases that I’m not a fan of such as “LSI Keywords” which has as much scientific credibility as Keyword Density, which is next to none. There were researchers from Bell Labs, in 1990, who wrote a white paper about Latent Semantic Indexing, which was something that was used with small (less than 10,000 documents) and static collections of documents (the web is constantly changing and hasn’t been that small for a long time.)

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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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Google’s Project Jacquard: Textile-Based Device Controls

Textile Devices with Controls Built into them

I remember my father building some innovative plastics blow molding machines where he added a central processing control device to the machines so that all adjustable controls could be changed from one place. He would have loved seeing what is going on at Google these days, and the hardware that they are working on developing, which focuses on building controls into textiles and plastics.

Outside of search efforts from Google, but it is interesting seeing what else they may get involved in since that is beginning to cover a wider and wider range of things, from self-driving cars to glucose analyzing contact lenses. I was surprised to see a web page from Levi’s showing a joint project from Google and Levis on their Project Jacquard.

This morning I tweeted an article I saw in the Sun, from the UK that was kind of interesting: Seating Plan Google’s creating touch-sensitive car seats that will switch on air con, sat-nav and change music with a BUM WIGGLE

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Citations behind the Google Brain Word Vector Approach

Cardiff-Tidal-pools

In October of 2015, a new algorithm was announced by members of the Google Brain team, described in this post from Search Engine Land – Meet RankBrain: The Artificial Intelligence That’s Now Processing Google Search Results One of the Google Brain team members who gave Bloomberg News a long interview on Rankbrain, Gregory S. Corrado was a co-inventor on a patent that was granted this August along with other members of the Google Brain team.

In the SEM Post article, RankBrain: Everything We Know About Google’s AI Algorithm we are told that Rankbrain uses concepts from Geoffrey Hinton, involving Thought Vectors. The summary in the description from the patent tells us about how a word vector approach might be used in such a system:

Particular embodiments of the subject matter described in this specification can be implemented so as to realize one or more of the following advantages. Unknown words in sequences of words can be effectively predicted if the surrounding words are known. Words surrounding a known word in a sequence of words can be effectively predicted. Numerical representations of words in a vocabulary of words can be easily and effectively generated. The numerical representations can reveal semantic and syntactic similarities and relationships between the words that they represent.

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