So far, we have dealt with explicit preferences such as ratings. However, much of the preference data that we might be able to collect is implicit feedback, where the preferences between a user and item are not given to us, but are, instead, implied from the interactions they might have with an item. Examples include binary data, such as whether a user viewed a movie, whether they purchased a product, and so on, as well as count data, such as the number of times a user watched a movie.
There are many different approaches to deal with implicit data. MLlib implements a particular approach that treats the input rating matrix as two matrices: a binary preference matrix, P, and a matrix of confidence weights, C.