Initial profile generation in recommender systems using pairwise comparison

Lior Rokach, Slava Kisilevich

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Most recommender systems, such as collaborative filtering, cannot provide personalized recommendations until a user profile has been created. This is known as the new user cold-start problem. Several systems try to learn the new users' profiles as part of the sign up process by asking them to provide feedback regarding several items. We present a new, anytime preferences elicitation method that uses the idea of pairwise comparison between items. Our method uses a lazy decision tree, with pairwise comparisons at the decision nodes. Based on the user's response to a certain comparison, we select on-the-fly what pairwise comparison should next be asked. A comparative field study has been conducted to examine the suitability of the proposed method for eliciting the user's initial profile. The results indicate that the proposed pairwise approach provides more accurate recommendations than existing methods and requires less effort when signing up newcomers.

Original languageEnglish
Article number6212388
Pages (from-to)1854-1859
Number of pages6
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume42
Issue number6
DOIs
StatePublished - 13 Jun 2012

Keywords

  • Decision trees
  • machine learning
  • recommender systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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