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.
['Roberto Pagano', 'Paolo Cremonesi', 'Martha Larson', 'Balázs Hidasi', 'Domonkos Tikk', 'Alexandros Karatzoglou', 'Massimo Quadrana']
The Contextual Turn: from Context-Aware to Context-Driven Recommender Systems