Recommended algorithms are boring and kills creativity, interest, and curiosity.

It clusters and groups us in isolated bubbles. Prevent us from gaining and discovering new knowledge

What are the recommended algorithms or recommendation systems? It’s a tool that predicts a user’s preferences of what may like or may not like any given topic. It filters and recommends content based on a personality-based approach to help you find content (video, music, movie, show, product) of interest to you. The recommendation system mostly used in commercial applications.

The real motive behind the recommended system

Big tech companies have designed a sophisticated recommendation system to continuously engage their users and keep them addicted to using their products for some time. The motive, in general, is the same among tech companies, but it differs from one to another. For example, Youtube recommends conspiratorial content that generates clicks and thus more revenue with ads. Twitter suggests a similar account of what you just followed.

The implications of the recommended system

Guillaume Chaslot, a former engineer at YouTube, has said that “ The problem is that the AI isn’t built to help you get what you want — it’s built to get you addicted to YouTube. Recommendations designed to waste your time.”

Recommendation systems are grouping and clustering us in the same bubble of a given topic. It’s good to watch/see recommended content related to the same subject. It helps to build a deep understanding, but it also gets boring and dull as it continuously keeping suggesting in the same sphere over and over.

Recommendation systems designed to serve as an alternative to a search engine to help users discover items they might not have found otherwise. Maybe, it serves as an assistant concierge to display products we can’t find. Yet when it comes to educational topics, knowledge, movies, documentaries, etc. These systems kill interest and curiosity.

Why do I see this way? Our systems are broken

Because tech giants are dictating us what we watch or read on their platform. It doesn’t trigger the brain to learn diversified topics that may stimulate it and gain various knowledge. Why not help a user seek and discover other information unrelated to what he/she reading/watching.

The recommendation systems becoming more like TV channels, provide content that the service sees fit. That supports my claim of these systems kill curiosity and interest by diverting the audience’s attention.

Diversify the recommendation system

By keeping an option for the user whether to find recommended suggestions of the same topic or seek various subjects, we are keeping the preference to the user to decide.

At the end of each video or article, there should be a two call to action:

1- Related topics

2- Various topics

Redesigning the recommended systems

I am very fond of various topics, the help of the engine to show me what knowledge out there is stimulating and fueling for creativity and curiosity. It’s fun to see videos/articles about how to make perfume, extracting plants, how to make healthy supplements, types of coffee, etc. There is a lot of stuff we don’t know these are interesting and amusing facts. You name it, from history to products.

The former CTA it gets boring, again, I stress its clustering us in separate bubbles. Diversity is fun, interesting, eye-opening, and illuminating. In my opinion, we shall start a movement to diversify algorithm engines. Here is a simple case study about how google could be a better educational search engine: https://www.ahmedali.one/portfolio/project-four-2lcdy-wbaxt