This paper introduces a rule-based personalization framework designed to integrate various personalization algorithms from adaptive hypermedia and recommender systems. The framework is demonstrated through its application in the educational online board Comtella-D, where it enhances user experience by recommending relevant discussions. The study evaluates different recommender strategies, explores usage behavior over time, and finds that even a small amount of user data can effectively generate accurate recommendations.