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 re...

Full description

Saved in:
Bibliographic Details
Published in:Operating systems review 2019-07, Vol.53 (1), p.46-51
Main Authors: Vengerov, David, Jalagam, Sesh
Format: Article
Language:English
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0163-5980
DOI:10.1145/3352020.3352028