Relevance feedback is a well-established approach to refine search results based on user input, but its comparative evaluation across different methods remains limited in practice. This demonstration paper introduces an interactive platform that supports and compares four relevance feedback methods—Rocchio, PicHunter, Polyadic Search, and SVM-based active learning—under consistent conditions. The primary goal is to enhance the understanding of how different relevance feedback methods affect retrieval performance from both a technical and user-centric perspective. The source code is available at https://github.com/francescascotti16/Demo-Relevance-Feedback , while the demonstration can be found at http://relevance-feedback.isti.cnr.it/ .