Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions

Gediminas Adomavicius, Alexander Tuzhilin

Research output: Contribution to journalReview article

6360 Scopus citations

Abstract

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommander systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less Intrusive types of recommendations.

Original languageEnglish (US)
Pages (from-to)734-749
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Volume17
Issue number6
DOIs
StatePublished - Jun 1 2005

Keywords

  • Collaborative filtering
  • Extensions to recommander systems
  • Rating estimation methods
  • Recommander systems

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