Recommendation systems (RS) and P2P are both complementary in easing large-scale data sharing: RS to filter and personalize users' demands, and P2P to build de-centralized large-scale data sharing systems. However, many challenges need to be overcome when building scalable, reliable and efficient RS atop P2P. In this work, we focus on large-scale communities, where users rate the con-tents they explore, and store in their local workspace high quality content related to their topics of interest. Our goal then is to provide a novel and efficient P2P-RS for this context. We exploit users' topics of interest (automatically extracted from users' contents and ratings) and social data (friendship and trust) as parameters to construct and maintain a social P2P overlay, and generate recommendations. The thesis addresses several related issues. First, we focus on the design of a scalable P2P-RS, called P2Prec, by leveraging collaborative- and content-based filter-ing recommendation approaches. We then propose the construction and maintenance of a P2P dynamic overlay using different gossip protocols. Our performance experi-mentation results show that P2Prec has the ability to get good recall with acceptable query processing load and network traffic. Second, we consider a more complex in-frastructure in order to build and maintain a social P2P overlay, called F2Frec, which exploits social relationships between users. In this new infrastructure, we leverage content- and social-based filtering, in order to get a scalable P2P-RS that yields high quality and reliable recommendation results. Based on our extensive performance evaluation, we show that F2Frec increases recall, and the trust and confidence of the results with acceptable overhead. Finally, we describe our prototype of P2P-RS, which we developed to validate our proposal based on P2Prec and F2Frec.
