An industrial data recommender system to solve the problem of data overload

Abstract

Getting the right data to the right decision-maker is a significant problem for many industrial companies. One of the main reasons is an overload of data. With the increasing amounts of industrial data this problem is becoming a bigger problem in the future. In order to address this challenge we propose the use of an Industrial Data Recommender System (IDRS). An IDRS recommends additional data to append to the data the decision-maker is currently working with, using techniques from the recommender systems domain like content-based and collaborative filtering. Using industrial cases we found that an IDRS is capable of suggesting useful information to the decision-maker. This additional information should help them to improve their decision-making.