I can find many resources on item-item or user-user similarity or collaborative filtering, but am having a hard time finding or knowing which terms to search for when combining them.
For example, a movie database that has a database of user profiles with features of the user such as location and age. Then a database of user rankings of items such as items purchased or returned. Finally a database of item features such as length of movie time and box office open profit.
So the matrices are: Users x User Features Items x Item Features Users x Item Purchases
First, I am having a hard time understanding how these can be combined. If I do a similarity matrix of the users and items, can these be used as a scaling factor to a collaborative filter based on userxitem purchases?
The second issue, I am not sure what it is called, so can not find it, but essentially I do not want latent factor modeling because I would like to manually weight user or item features. In one output, maybe the user feature of location should be weighted 2x a users age, and an item length of time should be weighted 1.5x the box office profits.