A collaborative filtering system (CF) aims at filtering huge amount of information, in order to guide users of web applications towards items that might interest them. Such a system, consists in recommending a set of personalized items for an active user, according to the preferences of other similar users. Existing methods, such as memory and Matrix Factorization (MF) approaches can achieve very good recommendation accuracy, unfortunately they are computationally very expensive. Applying such approaches to real-world applications in which users, items and ratings are frequently updated remains therefore a challenge. To address this problem, we propose a novel efficient incremental CF system, based on a weighted clustering approach. Our system is designed to provide a high quality of recommendations with a very low computation cost. In contrast to existing incremental methods, the complexity of our approach does not depend on the number of users and items. Our CF system is therefore suitable for dynamic settings, involving huge databases, in which available information evolves rapidly (i.e, submission of new ratings, update of existing ratings, appearance of new users and new items). Numerical experiments, conducted on several real-world datasets, confirm the efficiency and the effectiveness of our method, by demonstrating that it is significantly better than existing incremental CF methods in terms of both scalability and recommendation quality.