"What would be the best way to predict the movies I like?" is arguably too broad (perhaps not, it's ultimately for the community to decide, but my guess from my experience on CV is that it might be). There will be lots of possibilities
, and "best" is not well defined.
On the other hand, I don't think the level of "triviality" matters much for the site. There is no need for your project to be about death. Predicting movie preference is fine, and in fact, there has been a lot of academic work on more or less that topic within machine learning (e.g., recommender systems: CV, Wikipedia).
What might help here is to familiarize yourself with SE's processes for closing and reopening threads. Our help center has articles on closing, reopening, and how to ask a good question (you might also want to read our meta.CV thread on How to ask a “good” question on CrossValidated?). Relative to this conversation, a brief version is that a question can be put on hold by votes from 5 members of the community or from a moderator, if there are some concerns about the question. At that point, you can edit the question to address those concerns, and communicate with members in the comments. Editing your post bumps it into the reopen queue where members can view the issues and subsequent changes; they may vote to reopen the question or to leave it closed. If enough people vote one way or another, the issue is resolved (it is reopened or left closed). Notably however, this process takes time. You need 5 reopen votes from members, or 1 from a moderator, for your question to be reopened. On smaller sites like CV, this can often take a couple days. I recognize that is frustrating for you, but it is the way the system works.
In a comment below, you write:
"There will be lots of possibilities" - yes, this is true! And that was something I was really looking for, to read different approaches and see what works best (again, best in terms of R2). You all probably know kaggle, where it is also about to find the "best model" in terms of some goodness or lack of fit. To me, my question appears to be similar. And well, nobody shuts down kaggle or puts in on hold for being to unclear. I know I can't compare kaggle with CV, just writing it to explain what I was expecting. I hoped for different approaches
This is in fact something I was wondering about. It seemed like maybe you had a kaggle-type model in mind, where many people would try many things and post them, and you would pick the one that you liked best (for example, maybe because it gave you the best $R^2$ on a hold out sample you had). That is very much not what CV is about. CV is not kaggle. This is a site for questions about statistics (machine learning, etc.), not a place for people to analyze actual data (although that does happen on occasion in the course of answering a question about statistics). That makes your question off topic here, irrespective of any triviality or lack thereof.