A machine learning approach to recommending files in a collaborative work environment

被引：0

作者：

Vengerov D. ^{[1
]}

Jalagam S. ^{[1
]}

机构：

[1] Box Inc., Redwood City, CA

来源：

Operating Systems Review (ACM) | 2019年 / 53卷 / 01期

关键词：

Collaborative Filtering; Feature Selection; Machine Learning; Matrix Factorization; Transfer of Learning;

D O I：

10.1145/3352020.3352028

中图分类号：

学科分类号：

摘要：

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. © Copyright held by the owner/author(s). Publication rights licensed to ACM.

引用

页码：46 / 51

页数：5

共 6 条

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[6] Classification and Regression Methods in Spark ML

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