I'm building a recommender system where the number of products is rather low (around 50), and we can assume it'll stay the same for a long time.
I'm looking at two different way of tackling the problem:
- Using a matrix factorization technique.
- Treating it as a multi-class classification problem with a target of 50 different possible values. The features I'm using are the ones that the matrix factorization technique uses implicitly:
- Number of times a user has bought product 1.
- Number of times a user has bought product 2.
- Number of times a user has bought product 50.
In addition, I'm also able to add user features for the classification approach, like age, gender, and localization, among others.
Is there any reason/advantages to using the matrix factorization technique in this case? What are the advantages/disadvantages of both methods?