# How can we define missing rating in recommender system?

I was reading about collaborative filtering where we need to pass (user, item and rating) in case of matrix factorisation (SVD). Now, my question is given data of following form

 User | Item | Rating A | X1 | 1 A | X2 | 3 B | X2 | 4 C | X1 | 3 C | X3 | 2  we need to convert the it into

 U/I | X1 | X2 | X3 A | 1 | 3 | - B | - | 4 | - C | 3 | - | 3 

So, we need to replace all the - with the predicted value prior to applying svd on it. Now I would like to understand what are most practical or mostly adopted way to predict such missing rating: I am aware about following crude ways, but they are not prediction of value but they are mere replacement of missing value

1. Replace all missing values by neutral rating . (Say either 2 or 3 if rating are from 1-5).
2. Replace with mean rating of the movie.
3. Replace with average rating of that user. etc...