Unit 8200-linked Taboola and Outbrain push internet content

[yankeedoodle]

From the Wide Awake Gentile at the NWO Broadcast Corp
TABOOLA ,OUTBRAIN ,"CONTENT RECOMMENDORS " OR UNIT 8200 SPIES ?
https://nwobroadcastcorp.wordpress.com/2023/09/19/taboola-outbrain-the-israeli-owned-companies-recommending-content-or-unit-8200-spying-on-you/

A content recommendation engine is the process and platform that decides what content to recommend to individual users. Often times, this means showing pieces of content like blog articles or products to visitors on a website based on their user profile.

A content recommendation engine is a software solution that creates personalized user experiences by analyzing user and product data. The engine looks at a user's past online behavior, their likes and dislikes, and other key information, and uses that data to supply personalized content or make buying or viewing recommendations specific to that user.

Companies that want to provide a personalized experience for their online customers frequently employ some sort of content recommendation engine. This technology can create dynamic webpage content for visitors as well as recommend products for shoppers. The top websites today — including Amazon, Facebook, Google, Netflix — use content recommendation to better serve their users.

It is pretty much a given that at some point during your internet existence you have been confronted with content recommendation platforms, one of the most widespread forms of native advertising on the internet. Heck, it probably happens on a daily basis. Several times actually. Content recommendation platforms, the umbrella term that most accurately encompasses the different types we are about to describe, are those links you find at the bottom of an article or a blog post.



Notice the top and bottom right corner of each screenshot. Taboola and Outbrain. These are the two biggest content recommendation companies in the world, and they make up two of the most visible native advertising players on the web.

WordPress too seems to have entered into a deal with Outbrain





Adam Singolda spent seven years doing advanced encryption for the IDF (Unit 8200) before he founded Taboola in 2007, a recommendation engine that has an estimated value of $1.5 billion. Founders of the Web development platform Wix.com also served in 8200.  https://www.csmonitor.com/World/Passcode/2014/1205/Cybersecurity-unit-drives-Israeli-Internet-economy

Outbrain's Ori Lahav and Yaron Galai (both served in the Israeli navy)and probably worked in Unit 8200 too

Taboola generates 300 billion recommendations monthly ("Content You May Like") on sites such as NBC, USA Today, Die Welt, The Weather Channel, The Atlantic, Billboard.com and Fox Television.

. Outbrain serves more than 200 billion recommendations per month

As content recommendation platforms, or content discovery companies, Outbrain and Taboola place links beneath or next to articles or blog posts on a given website. Media companies and marketers pay Outbrain and Taboola to get their links posted on the publishers' site in an effort to drive traffic to their content. The content discovery company shares revenue with the publishers where the links appear.

Content discovery or content recommendation is big business in native advertising. In November 2014, Time Inc. struck a deal with Outbrain worth more than 100 million dollars to the magazine publisher. The partnership establishes Outbrain as the exclusive external provider for recommended links for Time Inc. websites such as Time.com and People.com.  https://adage.com/article/media/time-deal-outbrain-worth-100-million/295889

How does a content recommendation engine work?

All content recommendation engines need data on which to base their recommendations. These metrics can be about the user (demographic information, buying/viewing habits, etc.) or about the products (keywords, description, etc.). Some data is explicit (gathered from customer input); some is implicit (garnered from customer behavior, such as order history). 

2. Data storage
The dataset collected must be stored in some sort of database, such as an SQL database so it can execute the recommendation algorithm.

3. Data analysis
The content recommendation system then analyzes the stored data and looks for relationships between data points. This can take place in real time or via a non-dynamic batched analysis.

4. Data filtering
The final step in the content recommendation process filters the data to obtain the relevant information necessary to make an accurate recommendation to the user. This is typically done via some sort of algorithm — collaborative, content-based, or a hybrid of the two approaches.

Is Unit 8200 digitally spying on millions of Internet users via the billions of silly recommended ads placed in top websites by companies headed by "former" Unit8200 members ?