Recommender system using unsupervised machine learning for satisfaction surveys

Abstract

Satisfaction surveys are being used more and more by companies to improve their sales force. With the development of new technologies the piloting of these satisfaction surveys is digitized in a partial way. Piloting these surveys often involves the expertise of a human agent in order to make a judgment on the results obtained from satisfaction surveys. This is a tedious task for the decision-maker, as it faces a huge and heterogeneous amount of data. This problem may be mitigated by using a recommendation engine based on the unsupervised machine learning algorithm. This recommendation system (RS) will be oriented towards two axes: decision-making (DM) and machine learning (ML). In our approach, we use RS for consistency between the user and the recommended items. ML will allow us to include in our list of recommendations, unexpected items, items that are not derived from the algorithmic logic of the recommendation system and to make the system partially autonomous on decision-making (to less involving the recommendation engine). Our approach is divided into a) the recommendation process for decision-making, b) unsupervised ML and c) partial "empowerment" for decision-making.