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.@en

Pairwise learning-to-rank algorithms have been shown to allow recommendersystems to leverage unary user feedback. We proposeMulti-feedback Bayesian Personalized Ranking (MF-BPR), a pairwisemethod that exploits different types of feedback with an extendedsampling method. The feedback types are drawn from different“channels”, in which users interact with items (e.g., clicks,likes, listens, follows, and purchases). We build on the insight thatdifferent kinds of feedback, e.g., a click versus a like, reflect differentlevels of commitment or preference. Our approach differs fromprevious work in that it exploits multiple sources of feedback simultaneouslyduring the training process. The novelty of MF-BPRis an extended sampling method that equates feedback sources with“levels” that reflect the expected contribution of the signal. Wedemonstrate the effectiveness of our approach with a series of experimentscarried out on three datasets containing multiple typesof feedback. Our experimental results demonstrate that with a rightsampling method, MF-BPR outperforms BPR in terms of accuracy.We find that the advantage of MF-BPR lies in its ability to leveragelevel information when sampling negative items.@en