Collaborate with strangers to find own preferences

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

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

17 Scopus citations


We consider a model with n players and m objects. Each player has a "preference vector" of length m that models his grade for each object. The grades are 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 distributed and sequential algorithms to solve the problem with logarithmic cost overhead.

Original languageEnglish
Number of pages7
StatePublished - 2005
EventSeventeenth Annual ACM Symposium on Parallelism in Algorithms and Architectures - Las Vegas, NV, United States
Duration: 18 Jul 200520 Jul 2005


ConferenceSeventeenth Annual ACM Symposium on Parallelism in Algorithms and Architectures
Country/TerritoryUnited States
CityLas Vegas, NV


  • Billboard
  • Collaborative filtering
  • Electronic commerce
  • Probes
  • Randomized algorithms
  • Recommendation systems


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