Distributed collaborative filtering for peer-to-peer file sharing systems

Peer-to-peer networks are becoming more and more popular to share information such as, for ex-ample, multimedia files. Since this information is stored locally at the different peers, it is necessary to facilitate the search in an intelligent way. Collab-orative filtering is such a search technique that en-ables to incorporate the preferences of a user that can be learned from the download activities of the users. To be effective collaborative filtering requires a large database that captures these activities. Within a peer-to-peer network this is, however, not readily available. Here, we propose a collaborative filtering approach that is self-organizing and operates in a distributed way. Information about the similarity between mul-timedia files (items) is stored locally at these items in so called item-based buddy tables. We propose to use the language model (popular within information retrieval) to build recommendations for the different users based on the buddy tables of those items a user has downloaded previously (indicating the preference of the user). We have tested and compared our dis-tributed collaborative filtering approach to centralized collaborative filtering and showed that it has similar performance. It is therefore a promising technique to facilitate the search for information in peer-to-peer networks.