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Collaborative filtering(CF) recommender systems are among the most popular approaches to solving the information overload problem in social networks by generating accurate predictions based on the ratings of similar users. Traditional CF recommenders suffer from lack of scalability while decentralized CF recommenders (DHT based, gossip based etc.) have promised to alleviate this problem. Thus, in this thesis we propose a decentralized approach to CF recommender systems that uses the T-Mandoi:10.1109/socialcom-passat.2012.94 dblp:conf/socialcom/MagureanuDMM12 fatcat:ov273v4trnawtlzjz7lm44zane