Message Ranking in a Factory Setting Using Context and User Preference

In an industrial production setting, operators, administrators, and managing staff usually receive messages(human or machine generated) in shared electronic message pipeline. The larger the factory and/or the number of staff, the longer the message queue and communication drawbacks follow. This paper proposes a framework that ranks messages in a communication pipeline individually, for each user, based on contexts and user preferences. The method combines a flat set of rules and an optional learning mechanism that enables tuning these rules using reference data when available. Our method does not encounter the cold start problem, systems based on this framework provide acceptable rankings immediately, which is useful in cases where no ground truth is provided. The framework presented here ensures high scalability, new rules can be easily plugged into the system without affecting the general architecture, as well as high adaptability, where ranking parameters can be modified by administrators.