Recommender systems with non-binary grades

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

Abstract

We consider the interactive model of recommender systems, in which users are asked about just a few of their preferences, and in return the system outputs an approximation of all their preferences. The measure of performance is the probe complexity of the algorithm, defined to be the maximal number of answers any user should provide (probe complexity typically depends inversely on the number of users with similar preferences and on the quality of the desired approximation). Previous interactive recommendation algorithms assume that user preferences are binary, meaning that each object is either "liked" or "disliked" by each user. In this paper we consider the general case in which users may have a more refined scale of preference, namely more than two possible grades. We show how to reduce the non-binary case to the binary one, proving the following results. For discrete grades with s possible values, we give a simple deterministic reduction that preserves the approximation properties of the binary algorithm at the cost of increasing probe complexity by factor s. Our main result is for the general case, where we assume that user grades are arbitrary real numbers. For this case we present an algorithm that preserves the approximation properties of the binary algorithm while incurring only polylogarithmic overhead.

Original languageEnglish
Title of host publicationSPAA'11 - Proceedings of the 23rd Annual Symposium on Parallelism in Algorithms and Architectures
Pages245-252
Number of pages8
DOIs
StatePublished - 2011
Event23rd ACM Symposium on Parallelism in Algorithms and Architectures, SPAA'11 - San Jose, CA, United States
Duration: 4 Jun 20116 Jun 2011

Publication series

NameAnnual ACM Symposium on Parallelism in Algorithms and Architectures

Conference

Conference23rd ACM Symposium on Parallelism in Algorithms and Architectures, SPAA'11
Country/TerritoryUnited States
CitySan Jose, CA
Period4/06/116/06/11

Keywords

  • collaborative filtering
  • recommendation systemes

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