Real-Time Personalization Of Search Results - Deepstash

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Real-Time Personalization Of Search Results

Google’s current privacy policies discuss how the search engine currently creates personalized search results based on a user’s behaviour.

Google’s personalized search patent states that:

“…personalized search generates different search results to different users of the search engine based on their interests and past behaviour.”

We can clearly see this in action. Often used in conference presentations, proving this process is as simple as typing a string of queries into Google in one sitting and seeing how the results change depending on what you last searched.

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Capable of autonomously learning and solving problems, general AI (AGI) takes machine learning to the next level.

Google has been making steady progress in the way it connects users to the content they’re searching for, including these nine ways we know search engines are using machine learning right now.

At the moment, artificial superintelligence (ASI) still lands fully in the category of science fiction.

Google’s end goal is to use technology to provide users with a better experience. They don’t want to automate the entire process if that means the user won’t have the experience they are looking for.

While machine learning isn’t (and probably never will be) perfect, the more humans interact with it, the more accurate and “smarter” it will get.

RankBrain is the machine learning algorithm developed by Google that not only helps identify patterns in queries, but also helps the search engine identify possible new ranking signals.

Low-quality content typically has distinct similarities, such as:

This type of AI is designed to perform specialized tasks that must be “taught” to the algorithm (think Google’s search algorithms).

Back in 2016, Google declared its intention to become a “machine learning first” company. Since then, they’ve made steady strides toward that goal, launching Google AI in 2017 and rolling out BERT in 2019.

Every second, approximately 1087 photos are uploaded to Instagram, and 4000 are uploaded to Facebook. That’s hundreds of millions of photos being uploaded to those two social networks alone every day.

A huge influx of capital by tech giants, especially search engines, means that AI computing power is making rapid advancements in a range of sectors from healthcare to construction to marketing and search engine optimization.

While companies like OpenAI and Conversion.ai are moving toward developing general AI for natural language processing, there are currently no clear-cut examples of AGI.

It’s important for a search engine to be able to recognize how similar one piece of text is to another. This applies not just to the words being used but also their deeper meaning.

People don’t always speak like a machine would expect them to. We play with language to come up with new turns of phrases.

Just like its organic search results, Google wants to provide the most relevant ads for its individual users. According to Google U.S. patents on ad quality, machine learning can be used to improve an “otherwise weak statistical model.”

When you see search results that don’t include the keyword in the snippet, it’s likely due to Google using RankBrain to identify synonyms.

There are many reasons to fire up a search engine. Users may be searching to buy (transactional), research (informational), or find resources (navigational) for any given search.

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