We have information about what the user likes in our app and we want to recommend content to similar users even those who may not have explicitly like a particular content but are similar to those who have explicitly liked that content.
I was planning to use the implicit feedback variation of Alternating Least Squares using Apache Spark to implement a recommender system which will find me the additional users that I can target.
One of the tuning parameters is a boolean flag
nonnegative for non-negative matrix factorization.
I will be counting the number of likes per user and tag so I won't have negative values. Can I say
nonnegative=true or does it mean something completely different. My understanding is that the nonnegative constraint is for the values inside the two matrices which the algorithm will decompose my original matrix to, but I don't have the knowledge to know if those values will be non-negative for my scenario.
Ref: Spark ALS Algorithm. Not exactly since there is another API but good enough for this question.