Towards Inferring Ratings from User Behavior in BitTorrent Communities

Abstract

Peer-to-peer file-sharing has been increasingly popular in the last decade. In most cases file-sharing communities provide only minimal functionality, such as search and download. Extra features such as recommendation are difficult to implement because users are typically unwilling to provide sufficient rating information for the items they download. For this reason, it would be desirable to utilize user behavior to infer implicit ratings. For example, if a user deletes a file after downloading it, we could infer that the rating is low, or if the user is seeding the file for a long time, the rating is high. In this paper we demonstrate that it is indeed possible to infer implicit ratings from user behavior. We work with a large trace of Filelist.org, a BitTorrent-based private community, and demonstrate that we can identify a binary like/dislike distinction over the set of files users are downloading, using dynamic features of swarm membership. The resulting database containing the inferred ratings will be published online publicly and it can be used as a benchmark for P2P recommender systems.