Apache Spark ALS collaborative filtering results. They don't make sense

Question

I wanted to try out Spark for collaborative filtering using MLlib as explained in this tutorial: https://databricks-training.s3.amazonaws.com/movie-recommendation-with-mllib.htm

Answer 1

Collaborative Filtering just give you items that people, who have the same taste as you, really like. If you rate only kids movies, it doesn't mean that you will get recommended only kids movies. It just means that people who rated Toy Story, Jungle Book, Lion King, etc... as you did also like Life of Oharu, More, Who's Singin' Over There?, etc... You have a good animation on the wikipedia page: CF

I didn't read the link that you gave but one thing that you can change is the similarity measure you are using if you want to stay with collaborative filtering.

If you want recommendation based on your taste, you might try latent factor model like Matrix Factorization. Here the latent factor might discover that movie can be describe as features that describe the characteristics of rated objects. It might be that a movie is comic, children, horror, etc.. (You never really know what the latent factor are by the way). And if you only rate kids movies, you might get as recommendation others kids movies.

Hope it helps.