Tell me who i am: An interactive recommendation system

Noga Alon*, Baruch Awerbuch, Yossi Azar, Boaz Patt-Shamir

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We consider a model of recommendation systems, where each member from a given set of players has a binary preference to each element in a given set of objects: intuitively, each player either likes or dislikes each object. However, the players do not know their preferences. To find his preference of an object, a player may probe it, but each probe incurs unit cost. The goal of the players is to learn their complete preference vector (approximately) while incurring minimal cost. This is possible if many players have similar preference vectors: such a set of players with similar "taste" may split the cost of probing all objects among them, and share the results of their probes by posting them on a public billboard. The problem is that players do not know a priori whose taste is close to theirs. In this paper we present a distributed randomized peer-to-peer algorithm in which each player outputs a vector which is close to the best possible ap proximation of the player's real preference vector after a polylogarithmic number of rounds. The algorithm works under adversarial preferences. Previous algorithms either made severely limiting assumptions on the structure of the preference vectors, or had polynomial overhead.

Original languageEnglish
Title of host publicationSPAA 2006
Subtitle of host publication18th Annual ACM Symposium on Parallelism in Algorithms and Architectures
Pages1-10
Number of pages10
StatePublished - 2006
EventSPAA 2006: 18th Annual ACM Symposium on Parallelism in Algorithms and Architectures - Cambridge, MA, United States
Duration: 30 Jul 20062 Aug 2006

Publication series

NameAnnual ACM Symposium on Parallelism in Algorithms and Architectures
Volume2006

Conference

ConferenceSPAA 2006: 18th Annual ACM Symposium on Parallelism in Algorithms and Architectures
Country/TerritoryUnited States
CityCambridge, MA
Period30/07/062/08/06

Keywords

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

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