I'm a newbie in machine learning, so excuse me in advance). I have an idea to make NN that can estimate visual pleasantness of arbitrary image. Like you have a bunch of images that you like, you train NN on them, then you show some random picture to NN and it estimates whether you'll like it or not. I wonder if there is any pervious effort made in this direction.
This question reminds me of a project I saw that used Deep Learning to rate selfies on twitter. But a quick google search shows that there are plenty of projects that are much closer to what you are interested in:
Deep Understanding of Image Aesthetics (with data and model linked)
and probably dozens more.
Of course if you are interested in predicting subjective pleasantness the above is only a beginning. In that case you may also take a look at recommender systems.
I see a main concern with the problem you show and that is the subjectiveness of the term like, what I like is not the same of what you don't like, maybe I like more ciricular shapes and lou like best rectangular ones. The main problem is that with such an subjective label, is difficult to create a global model.
I don't think anyone has done it yet,but you could try. A way you could implement it is having a quite efficient CNN trained on the things you like,then your program should ask the user if he does like some images and on the answers he will give, your program will finetune the original network and then with the fresh-trained one you should obtain good results.