Machine Learning with Spark - Second Edition

Implicit Matrix Factorization

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.