  Binary relevance is a simple approach to solve multi-label learning problems
where an independent binary classifier is built per each label. A common
challenge with this in real-world applications is that the label space can be
very large, making it difficult to use binary relevance to larger scale
problems. In this paper, we propose a scalable alternative to this, via
transforming the multi-label problem into a single binary classification. We
experiment with a few variations of our method and show that our method
achieves higher precision than binary relevance and faster execution times on a
top-K recommender system task.
