Abstract

Peer-to-peer networks are becoming more and more popular to share information such as, for example, multimedia files. Since this information is stored locally at the different peers, it is necessary to facilitate the search in an intelligent way. Collaborative filtering is such a search technique that enables 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 peerto-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 multimedia 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 distributed 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. 1

Citations