The Contextual Turn: From Context-Aware to Context-Driven Recommender Systems

Roberto Pagano, Paolo Cremonesi, Martha Larson, Balázs Hidasi, Domonkos Tikk, Alexandros Karatzoglou, Massimo Quadrana

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

25 Citations (Scopus)


A critical change has occurred in the status of context in recommender systems. In the past, context has been considered 'additional evidence'. This past picture is at odds with many present application domains, where user and item information is scarce. Such domains face continuous cold start conditions and must exploit session rather than user information. In this paper, we describe the `Contextual Turn?: the move towards context-driven recommendation algorithms for which context is critical, rather than additional. We cover application domains, algorithms that promise to address the challenges of context-driven recommendation, and the steps that the community has taken to tackle context-driven problems. Our goal is to point out the commonalities of context-driven problems, and urge the community to address the overarching challenges that context-driven recommendation poses.
Original languageEnglish
Title of host publicationRecSys'16 Proceedings of the 10th ACM Conference on Recommender Systems
EditorsS. Sen, W. Geyer
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages4
ISBN (Electronic)978-1-4503-4035-9
Publication statusPublished - 2016
Event10th ACM Conference on Recommender Systems, RecSys 2016 - MIT, Boston, MA, United States
Duration: 15 Sep 201619 Sep 2016


Conference10th ACM Conference on Recommender Systems, RecSys 2016
Country/TerritoryUnited States
CityBoston, MA
Internet address


  • position paper
  • context
  • recommender systems

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