Maximizing aggregate recommendation diversity: A graph-theoretic approach

Gediminas Adomavicius, Young Ok Kwon

Research output: Contribution to journalConference articlepeer-review

46 Scopus citations

Abstract

Recommender systems are being used to help users find relevant items from a large set of alternatives in many online applications. Most existing recommendation techniques have focused on improving recommendation accuracy; however, diversity of recommendations has also been increasingly recognized in research literature as an important aspect of recommendation quality. This paper proposes a graph-theoretic approach for maximizing aggregate recommendation diversity based on maximum flow or maximum bipartite matching computations. The proposed approach is evaluated using real-world movie rating datasets and demonstrates substantial improvements in both diversity and accuracy, as compared to the recommendation re-ranking approaches, which have been introduced in prior literature for the purpose of diversity improvement.

Original languageEnglish (US)
Pages (from-to)3-10
Number of pages8
JournalCEUR Workshop Proceedings
Volume816
StatePublished - Jan 1 2011
EventWorkshop on Novelty and Diversity in Recommender Systems, DiveRS 2011 - At the 5th ACM International Conference on Recommender Systems, RecSys 2011 - Chicago, IL, United States
Duration: Oct 23 2011Oct 23 2011

Keywords

  • Aggregate diversity
  • Collaborative filtering
  • Graph-based algorithms
  • Recommendation diversity

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