MochaStar Media and the Recommendation Engine Model

We are launching our media division within The Manufactured Solution called MochaStar Media which is not a new name but will be hitting the mass market soon. This will be an AI-first management agency, advertising and marketing firm, and media company all rolled up into one.

This is a major ambition and we know the long-term investment and sustained growth and development will pay off big. AI technology will get better and reach more and more. I want to give you a hint about folks talking “AI slop” – those be the ones talking down on new paradigms like AI but behind the scenes, trying to get it up and running themselves.

One of the recurring issues we see with entrepreneurs is they are struggling to break into markets and connect with their customers. Trust me, I ran into the same wall. One thing I learned early on is yall are being gatekeep by characters out here and blocked from your blessings on purpose.

MochaStar Media is going to need marketing and promotions to get our artist out there. Our solution to this is our recommendation engine and I want to introduce you to the technology to understand it for yourself to see how to move forward. The days of begging to be written up in a magazine or appear on radio for some attentions are past us – we got to have strategies to bypass gatekeepers.

First Thing – Learn to Build It Yourself.

Never rely on someone to review your product before you release it to the market. Don’t go around looking for validation and acceptance from a few people like those at your school, job, trade association – cats be jealous and envious and going to hate and downplay or go silent withholding praise to take you down. That’s when you learn your first lesson about your circle was really a hate-you syndicate that you unknowingly created to block your blessings.

In the era of the self-ecosystem, you learn to build out the infrastructure and mechanisms to promote and reach your customers.

We don’t need to ask to appear in a magazine; we going to create our own AI Avatar magazine that feature AI-artists in music, AI agents for business, artwork, vTubers, and other AI assets. Interviews with Ben Banneker and stuff like that. We going to own all of what we doing and not depending on anybody in our self-ecosystem and you should have the same mindset.

This is part of our marketing toolkit. Magazine, email newsletter, personal blog, offline giveaways, digital advertising, video trailers, interviews with Georgette and Sandi show, booster clubs and so on. No different than the traditional industry.

But here is the thing and where many of us get lost at – we can create all these touchpoints and toolbox items for promotions, but how do we get this to our customer base? The answer is establishing a recommendation engine.

Recommendation Engine Explained

The high-level definition is a recommendation engine pushes content to customers using a contextual system. Let’s get into detail of each component to help you understand further.

Content Repository. These are media assets from advertisement, products listings, services, content such as blog articles, podcasts, e-book, or social media postings that can be promoted to a target audience to gain more views and conversion to customers.

Contextual Tag System. This is a tagging system using our graph node system to create relationships. AI modeling can take our graph system and create more complex algorithms to connect nodes for relationship. This ties the content to the customer as a likely match. Context can be tags but it can also be focus – what area the customer is focused on right now – are they looking at handbags, a trip to Japan?

Business Partners. These are going to be our partners who help us transmit the content to customers. This can be a retail store that need promotional posters, podcasters that need AI avatars to interview, a video feed of a trailer for a YouTuber promoting AI artists weekly.  

Customers. This is the person or business that we intent to present with an accurate touchpoint and call to action such as sign up for the newsletter, or purchase this item, or book an appointment or reservation.

Some people would think this is an advertisement engine that was already described in the MochaStar advertisement model and that is correct. See, that’s the problem I brought up earlier. Social media feeds are really the same as the advertisement process but what they do is artificially throttle and limit your postings then sell you “advertising” to deliver content the way they could have just done it in the first place.  

That’s what we don’t want to do – we are going to push content directly and full throttle to your customers using our engine because at the end of the day, the more customers you have, the bigger our platform become so it doesn’t make sense to throttle, unless we on some “wokeness” mess or bougie stuff.

Recommendation and Matching Engine

The recommendation engine is part of our core processors to drive economic empowerment in a region. The other engine is a matching engine to match buyers and sellers. The matching engine is part of the DBEXX division of marketplaces.

Notice we have two economic processing engines – a marketing engine to promote products and services, and a matching engine to match the best seller offer to the best buyer bid. Notice these other clowns talking up economic empowerment are still around and a joke. Both these engines were learned from Asia as I frequented the JPX stock exchange and saw how TikTok was better at matching content with their users – Afroasiatic movement.

In summary, our AI artists will need a marketing strategy to get them out to customers. Our platform will be available to anybody who using our ecosystem except folks have to pay for the service. The TMS founding members don’t have to pay for anything; they supported me and the rest of yall did not.

I hope this help you understand how real we are over here and our stuff is real. We are incorporating AI deep-thinking to help us make better matching algorithms but this is the overall strategy to make sure our AI avatars can reach the market and no one is in the way of our blessings.