Comparison-based interactive collaborative filtering

Yuval Carmel, Boaz Patt-Shamir

Research output: Contribution to journalArticlepeer-review


In this work we study the interactive model of comparison-based collaborative filtering. Each player prefers one object from each pair of objects. However, revealing what is a player's preference between two objects can be done only by asking the player specifically about that pair, an action called probing. The goal is to (approximately) reconstruct the players' preferences with the smallest possible number of probes per player. The per-player number of probes can be reduced if there are many players who share a similar taste, but a priori, players do not know who to collaborate with. In this work, we present the model of comparison-based interactive collaborative filtering, analyze a few possible taste models and present distributed algorithms whose output is close to the best possible approximation to the players' taste.

Original languageEnglish
Pages (from-to)40-49
Number of pages10
JournalTheoretical Computer Science
StatePublished - 16 May 2016


  • Collaborative filtering
  • Parallel algorithms
  • Recommender systems
  • Social networks

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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