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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ormándi, R. Hegedus, I. Csernai, K. Jelasity, M. |
| Copyright Year | 2010 |
| 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. |
| Starting Page | 217 |
| Ending Page | 222 |
| File Size | 192438 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424472161 |
| ISSN | 15244547 |
| e-ISBN | 9781424472178 |
| DOI | 10.1109/WETICE.2010.41 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-28 |
| Publisher Place | Greece |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Recommender systems Artificial intelligence Spatial databases Statistical learning Collaborative work TV recommendation peer-to-peer database |
| Content Type | Text |
| Resource Type | Article |
| Subject | Hardware and Architecture Software |
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