We focus on peer-to-peer (P2P) content recommendation for on-line communities, where social relationships between users can be exploited as a parameter to increase the trust of recommendation. Most of the existing solutions establish friendship relationships based on users behavior or declared trust. In this paper, we propose a novel P2P recommendation approach (called F2Frec) that leverages content and social-based recommendation by maintaining a P2P and friend-to-friend network. This network is used as a basis to provide useful and high quality recommendations. Based on F2Frec, we propose new metrics, such as usefulness and similarity (among users and their respective friend network), necessary to enable friendship establishment and to select recommendations. We define our proposed metrics based on users' topic of interest and relevant topics that are automatically extracted from the contents stored by each user. Our experimental evaluation, using the TREC09 dataset and Wiki vote social network, shows the benefits of our approach compared to anonymous recommendation. In addition, we show that F2Frec increases recall by a factor of 8.8 compared with centralized collaborative filtering.