Recommendation Systems - (Private Browsing) Recommendation systems are ubiquitous in e-commerce and internet applications.
However, most recommender systems have been tuned to achieve low error rates for straightforward prediction tasks (predict the rating for a movie, predict whether the item will be purchased, etc.) — e.g., the Netflix Prize, but in many domains, recommendation systems forms a multidimensional problem with several constraints. I have been looking at some of these problems from the perspective of course recommendations, for the CourseRank project at Stanford. (CourseRank was launched as a startup company in 2010, and then acquired by Chegg.
The CourseRank technology is being used at over 500 universities in the United States.) SKYTREE The Machine Learning Company - (Private Browsing) k0pv0.jpg (JPEG Image, 2400 × 1507 pixels) - Scaled (42.