A Machine Learning Approach to Recommending Files in a Collaborative Work Environment

Recommendation of items to users is a problem faced by many companies in a wide spectrum of industries. This problem was traditionally approached in a one-shot manner, such as recommending movies to users based on all the movie ratings observed so far. The evolution of user activity over time was relatively unexplored. This paper presents a Machine Learning approach developed at Box Inc. for making repeated recommendations of files to users in a collaborative work environment. Our results on historical data show that this approach noticeably outperforms the approach currently implemented at Box and also the traditional Matrix Factorization approach.