Prediction of user actions with an Association Rules and two Neural Network models in a configuration environment

Sammanfattning

Extensive configurators might suffer from latency due to their complexity - something which could hurt user experience and, in turn, sales. Machine learning models could potentially learn the patterns of user actions by looking at past configuration sessions. The models would then be able to predict the next action in a configuration session by looking at a few, past actions which can allow for pre-caching of data and, in the end, reduce latency. This thesis aims at testing the predictive capabilities of an Association Rules model and a Long Short-Term Memory and a Gated Recurrent Unit network. It found that all three models are capable of predicting future actions with the Gated Recurrent Unit producing the most accurate predictions.