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We present a novel framework for studying recommendation algorithms in terms of the 'jumps' that they make to connect people to artifacts. This approach emphasizes reachability via an algorithm within the implicit graph structure underlying a recommender dataset and allows us to consider questions relating algorithmic parameters to properties of the(More)
We present a novel framework for evaluating recommendation algorithms in terms of the 'jumps' that they make to connect people to artifacts. This approach emphasizes reach-ability via an algorithm within the implicit graph structure underlying a recommender dataset, and serves as a complement to evaluation in terms of predictive accuracy. The framework(More)
We explore the conflict between personalization and privacy that arises from the existence of weak ties. A weak tie is an unexpected connection that provides serendipitous recommendations. However, information about weak ties could be used in conjunction with other sources of data to uncover identities and reveal other personal information. In this article,(More)
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