Collaborate with strangers to find own preferences

Baruch Awerbuch, Yossi Azar, Zvi Lotker, Boaz Patt-Shamir, Mark R. Tuttle

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We consider a model with n players and m objects. Each player has a "preference vector" of length m, that models his grades for all objects. The grades are initially unknown to the players. A player can learn his grade for an object by probing that object, but performing a probe incurs cost. The goal of a player is to learn his preference vector 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 a sequential and a parallel algorithm to solve the problem with logarithmic cost overhead.

Original languageEnglish
Pages (from-to)27-41
Number of pages15
JournalTheory of Computing Systems
Issue number1
StatePublished - Jan 2008
Externally publishedYes

Bibliographical note

Funding Information:
An extended abstract of this work appeared in the 17th Ann. ACM Symp. on Parallelism in Algorithms and Architecture, Las Vegas, Nevada, July 2005. Research of B. Awerbuch supported by NSF grant ANIR-0240551 and NSF grant CCR-0311795. Research of Y. Azar supported in part by the German-Israeli Foundation and by the Israel Science Foundation. Research of B. Patt-Shamir supported in part by Israel Ministry of Science and Technology and by the Israel Science Foundation.


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