Is it Item based or content based Collaborative filtering?

I am currently working on an existing system that recommends items that are similar to previous items that the user has liked.

It uses Alternating least squares Collaborative Filtering to find feature vectors of users and items. It then uses the feature vectors of the items and uses the cosine similarity measure to find similar items to it.

However, I would like some clarification as to whether this is item based CF or content based filtering? My inclination is that it is both. Since it is using a similarity measure to compare items, but the items are on the content of the feature vector?

Thanks,

If I understand correctly that you extract feature vectors for the items from users-like-items data, then it is pure item-based CF.

In order to be content based filtering, features of the item itself should be used: for example, if the items are movies, content based filtering should utilize such features like length of the movie, or its director, or so on, but not the features based on other users' preferences.