P2Prec: a Social-based P2P Recommendation System for Large-scale Data Sharing

Fady Draidi 1, 2, * Esther Pacitti 2 Patrick Valduriez 1, 2 Bettina Kemme
* Corresponding author
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : We propose P2Prec, a P2P recommendation system for large-scale data sharing, which exploits friendship links. The main idea is to recommend high quality contents related to query topics and contents of friends (or friends of friends), who are expert on the topics related to the query. Expertise is implicitly deduced based on the contents stored by a user. To exploit friendship links, we rely on Friend-Of-A-Friend (FOAF) descriptions. To disseminate information about experts, we propose new semantic-based gossip algorithms that provide scalability, robustness, simplicity and load balancing. By using information retrieval techniques, we propose an efficient query routing algorithm that recommends the best peers to serve a query. In our experimental evaluation, using the TREC09 dataset and Wiki vote social network, we show that using semantic gossiping increases recall by a factor of 2.5 compared with well known random gossiping. Furthermore, P2Prec has the ability to get reasonable recall with acceptable query processing load and network traffic.
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Fady Draidi, Esther Pacitti, Patrick Valduriez, Bettina Kemme. P2Prec: a Social-based P2P Recommendation System for Large-scale Data Sharing. [Research Report] 2010. ⟨inria-00543298⟩

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