The following algorithms are supported by Spark ML:
- Collaborative filtering
- Alternating Least Squares (ALS): Collaborative filtering is often used for recommender systems. These techniques aim to fill the missing entries of a user-item association matrix. The spark.mllib currently supports model-based collaborative filtering. In this implementation, users and products are described by a small set of latent factors that can be used to predict missing entries. The spark.mllib uses the ALS algorithm to learn these latent factors.
- Clustering: This is an unsupervised learning problem where the aim is to group subsets of entities with one another based on the notion of similarity. Clustering ...
