With the steady increase in the use of online platforms, ranging from social media to streaming services, it becomes essential to personalize user experiences in order to induce continued use. In this context, recommender systems emerge and evolve, leveraging user and object data to increase user engagement. The central objective of this thesis is the design and implementation of a custom recommendation system for the Weeshop application, leveraging data collected from this platform. Initially, a hybrid system integrating content-based, collaborative-based and session-based parts was conceived. However, due to the scarcity of user data, the implementation of the collaborative component was discarded.