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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.

Data consumption has changed significantly in the last 10\ud years. The digital revolution and the Internet has brought an abundance\ud of information to users. Recommender systems are a popular means of\ud finding content that is both relevant and personalized. However, today’s\ud users require better recommender systems, able of producing continuous\ud data feeds keeping up with their instantaneous and mobile needs. The\ud CrowdRec project addresses this demand by providing context-aware,\ud resource-combining, socially-informed, interactive and scalable recommendations.\ud The key insight of CrowdRec is that, in order to achieve\ud the dense, high-quality, timely information required for such systems, it\ud is necessary to move from passive user data collection, to more active\ud techniques fostering user engagement. For this purpose, CrowdRec activates\ud the crowd, soliciting input and feedback from the wider community