The first emojis were created in 1999. Since then, their popularity constantly raised in communication systems. Being images representing either an idea, a concept, or an emotion, emojis are available to the users in multiple software contexts: instant messaging, emails, forums, and other types of social medias. Their usage grew constantly and, associated to the constant addition of new emojis, there are now more than 2,789 standard emojis since winter 2018. To access a specific emoji, scrolling through huge emoji librairies or using a emoji search engines is not enough to maximize their usage and their diversity. An emoji recommendation system is required. To answer this need, we present our research work focused on the emoji recommendation topic. The objectives are to create an emoji recommender system adapted to a private and informal conversational context. This system must enhance the user experience, the communication quality, and take into account possible new emerging emojis. Our first contribution is to show the limits of a emoji prediction for the real usage case, and to demonstrate the need of a more global recommendation. We also verify the correlation between the real usage of emojis representing facial expressions and a related theory on facial expressions. We also tackle the evaluation part of this system, with the metrics' limits and the importance of a dedicated user interface. The approach is based on supervised and unsupervised machine learning, associated to language models. Several parts of this work were published in national and international conferences, including the best software award and best poster award for its social media track.