I was planning to make an artwork recommendation system as a project by using the WikiArt open source dataset available on kaggle, I'm still looking for datasets which might already have user ratings but is it possible to make a recommender without any user ratings? The dataset currently has these features. Any help or insight is appreciated. Thanks.
Generally speaking, there are two types of recommender systems. One is community-based, the other is content-based.
In community-based, you are effectively telling a user, "you are a young female, here is content that other young females enjoyed".
In content-based, you are effectively telling a user, "you liked content A, here is content B that was liked by people who also enjoyed content A".
So, in your case, you would be looking to build a content-based system. That means that you will have to build a meta-data set based on user actions throughout your site. When someone watched content A, what did they watch after that? And then after that? What content made them leave? What content made them sign up for your site or buy something or do some other action on your site? What categories of content are likely to be watched together?
When you can gather that type of metadata then you will be in a better position to create a model. Note that I believe that you can extract this from the sample data you posted, it's just a matter of how you organize it.
First you need to clarify what kind of recommendation you want to make.
The reason for the rating is to be able to associate an item to a user preference. You can avoid a rating and design the recommendation as a classification problem where you classify a product to some event (someone would like the artwork or not), but this still needs you to design a label based on some information.
In summary yes you can do without a rating but you still need some information on user preference or whatever the label will be.