Name
Abstract | ||
|---|---|---|
We present the first personalized peer-to-peer top-k search protocol for a collaborative tagging system. Each peer maintains relevant personalized information about its tagging behavior as well as that of its social neighbors, and uses those to locally process its queries. Extensive experiments based on a real-world dataset crawled from del.icio.us shows that very little storage at each peer suffices to get almost the same results as a hypothetical centralized solution with infinite storage. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1578002.1578003 | SNS |
Keywords | DocType | Citations |
social neighbor,tagging behavior,hypothetical centralized solution,personalized peer-to-peer top-k processing,real-world dataset,relevant personalized information,extensive experiment,personalized peer-to-peer top-k search,collaborative tagging system,infinite storage | Conference | 3 |
PageRank | References | Authors |
0.38 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao Bai | 1 | 106 | 8.91 |
| Marin Bertier | 2 | 382 | 24.31 |
| Rachid Guerraoui | 3 | 6364 | 430.90 |
| Anne-Marie Kermarrec | 4 | 6649 | 453.63 |