Can a recommendation system be built without any user ratings?

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.

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.

This is a common issue for recommender system refered to as cold start. You can look it up there are some solutions suggested out there but I have never tested them out. Have a look at this paper:

http://www.cs.toronto.edu/~mvolkovs/nips2017_deepcf.pdf

Yes. You can recommend item similar to the item user selected without having users profiles. The similarity is based on items attributes and the approach often called More Like This. MLT is one of the variants of Content-Based Filtering.

See for instance:

• "Evaluating Item-Item Similarity Algorithms for Movies"
• "Finding Similar Movies: Dataset, Tools, and Methods"