Conference Paper

Collaborate with strangers to find own preferences.

DOI: 10.1007/s00224-007-9016-7 Conference: SPAA 2005: Proceedings of the 17th Annual ACM Symposium on Parallelism in Algorithms and Architectures, July 18-20, 2005, Las Vegas, Nevada, USA
Source: DBLP

ABSTRACT Abstract We consider a model with n players and m objects. Each player has an unknown,grade for each object, modeled by a “preference vector” of length m. A player can learn his grade for an object by probing that object, but performing a probe incurs cost. The goal of the players is to learn their own evaluations of objects with minimal cost, by adopting the results of probes performed by other players. To facilitate communication, we assume that players collaborate by posting their grades for objects on a shared billboard: reading from the billboard is free. We consider players whose preference vectors are popular, i.e., players whose preferences are common to many other players. We present distributed and sequential algorithms to solve the problem with logarithmic cost overhead. Submitted as a regular presentation. Please consider as a brief announcement as well.

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