Most content-recommendation systems you interact with are collaborative; with data about what users like, they use "likes" from one user as "recommendations" fo

Most content-recommendation systems you interact with are collaborative; with data about what users like, they use "likes" from one user as "recommendations" for another user, when those users generally like the same things. So if I like content A, B, C, and you like content A, B, D, they might recommend C to you and D to me. This approach is (comparatively) easy to build, since your recommendation system never needs to actually look at the content directly, just at whether users like it or not. There are problems with this approach, though, and a big one is the "cold start" problem–what if you have content in your system that few (or no) users have interacted with? Even though you might have information about the content available to you (like a text description, or, in this case, a poster/cover image), a collaborative system has no way to read this information, and so it can't recommend the content to users until people have found it organically first. So, a cooler option is to build a system that actually understands the content it recommends. In this case, there was an existing project called Illustration2Vec that used a deep neural net to predict tags from anime images. These authors have built a hybrid recommendation system that uses collaborative filtering when there is a lot of data available, and then uses tag similarity (getting the tags by running Illustration2Vec on the posters/cover images) when data is sparser. It can also "explain" the results by telling you which tags it used to give you the recommendation. The authors are using data from (and presumably, incorporating the resulting system into) a French anime/manga site called Mangaki (https://mangaki.fr/about/en https://mangaki.fr/about/en).