Improving aggregate recommendation diversity using ranking-based techniques

Gediminas Adomavicius, Young Ok Kwon

Research output: Contribution to journalArticlepeer-review

497 Scopus citations

Abstract

Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating data sets and different rating prediction algorithms.

Original languageEnglish (US)
Article number5680904
Pages (from-to)896-911
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Volume24
Issue number5
DOIs
StatePublished - 2012

Bibliographical note

Funding Information:
The research reported in this paper was supported in part by the US National Science Foundation (NSF) grant IIS-0546443.

Keywords

  • Recommender systems
  • collaborative filtering.
  • performance evaluation metrics
  • ranking functions
  • recommendation diversity

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