I have millions of user ratings on about 2k products. I want to use Machine Learning to analyse these ratings and recommend products to users based on other users ratings of the same and different products.
i.e. user A likes 1,2 & 3, user B likes 1&2 so probably likes 3 based on A's ratings.
Is this possible to do through AWS ML? I'm looking at AWS ML as I'm a developer, not a data scientist and looking to keep this simple.
I've been playing with this all day and think I have figured it out, but just looking for some further advise / guidance.
Based on my experimenting so far, the following format of dataset is what AWS needs
CustomerID, Prod1Rating, Prod2Rating, Prod3Rating,.....
And then I would create a model for each product with the target on each being the row containing that products ratings. To make a recommendations engine from this I then just need to loop through every product for a user and ask each model the score.