I'm starting to work in a project that will have a recommender system as one of its components. I'm trying to figure out if I have the right type of data for the recommender.
The data contains ratings from implicit feedback. That is, data is either $1$ if there has been interaction (between the user and the item), or unknown if there has not been interaction. Interaction in this problem would be that the user has clicked/viewed the item.
I have about 124K users, 4,2K items, and 160K ratings. The sparsity is 99.96%.
The amount of rating per user is like this:
Min. 1st Qu. Median Mean 3rd Qu. Max. 1.000 1.000 1.000 1.291 1.000 81.000
The amount of ratings per item is like this:
Min. 1st Qu. Median Mean 3rd Qu. Max. 1.00 1.00 3.00 37.59 11.00 13570.00
Clearly a majority of the users have only rated (interacted) with one item. While only a 25% of the items have a single rating.
My questions are:
- Which levels of sparsity (amount of user-item known ratings) are typical for recommender systems?
- How do I decrease the sparsity of my rating matrix? Should I remove users with few ratings, should I try to gather more data?