Peer-to-Peer (p2p) networks are used for sharing content by millions of users. Often, meta-data used for searching is missing or wrong, making it difficult for users to find content. Moreover, searching for new content is almost impossible. Recommender systems are unable to handle p2p data due to inherent difficulties, such as implicit ranking, noise and the extreme dimensions and sparseness of the network. This paper introduces methods for using p2p data in recommender systems. We present a method for creating content-similarity graph while overcoming inherent noise. Using this graph, a clustering method is presented for detecting proximity between files using the "wisdom-of-the-crowds". Evaluation using songs shared by over 1.2 million users in the Gnutella network, shows that these techniques can leverage p2p data for building efficient recommender systems.
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