The pervasive use of Peer-to-Peer (P2P) systems and the growing demand on personalization for consumers has made future business focus on the niche market instead of the mass market. The recommender system, which is able to timely select data of the interest to each individual user, has become the key to any successful business. However, currently most recommendation systems are based on a centralized architecture; nonetheless, they are not suitable for P2P environments. In this article, we propose a distributed semantic P2P overlay, which can provide music search and recommendation services by considering both of user preference and diversity of interests. We then propose a 3C hybrid recommendation procedure that adapts three traditional filtering techniques to fitting the requirements in a distributed semantic overlay. First, we choose a set of proper meta-data to represent a music object and use them to construct the characteristic-vector-based content filter. Second, a dominant attribute, which is one of the attributes in the characteristic vector of a music object, is used to build the profile of a peer. With the idea of the social network, a P2P profile-based collaborative filter is proposed. Finally, we explore the item-to-item relationship to construct a history-based cooperative filter. We use simulations and a real database called AudioScrobbler, which tracks users' listening habits, to evaluate the performance of the recommendation system. The results demonstrate the effectiveness of the proposed approach compared with existing recommendation systems.