The mathematical form of the recommender system? I think it’s
I – Map the user’s vector + their features in a high dimensional vector space
II – Estimate the goodness of all the relevant choices in that space
III – Return the choice(s) with the highest goodness value.

The articles I looked at online suggested that most Internet radio for cars is based on using a mobile phone Internet access. If there was a way to grab & store the mobile account info/chip & then operate without fussing with the phone itself that would enhance the auto experience. Developing whatever is required/possible for that is also a good project.

I suggest trying to create a bunch of play data & mockups to get a feel for what works & what the issues are prior to seeing whether GT staff’s naive ideas about user behavior match what’s found in the field. If the system works great for people like good team staff then the rest is not so hard.

Clarifying my remarks/thoughts about “education” channels…

I meant education in the sense of adult enrichment. For all ages. I wasn’t thinking about any direct connection to Good Team edu missions which must focus on mastery/study/testing etc. Having raised the question, what should be the connection? My thought is that edu incorporates a lot of soft material into standard curriculums. This includes watching movies, lighter reading, etc. It would be appropriate to designate special channels that support particular curriculum stages & connecting that with teachers & the ability to confirm that particular students put in the time with the set of channels that match their study course.

Privacy/Security Features:

For stream providers – make it super quick/ez to set up a virtual identity that hides their outside identity & gives them corporate-like legal protection from whatever arises. They understand they can change details later in ways that persist their fans.

For listeners – keep their habits private unless they share particular things. Give them a way to earn meta-provider stream status – has to be earned & maintained in some meaningful way from the POV of the commercial platform interests, and the internal logic of it has to be net positive to the main stream providers. If implemented correctly it should enhance the attractiveness of choices with smart human critic input.