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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)
Recommender systems have become paramount to customize information access and reduce information overload. They serve multiple uses, ranging from suggesting products and ar-tifacts (to consumers), to bringing people together by the connections induced by (similar) reactions to products and services. This thesis presents a graph-theoretic model that casts(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)
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)
Recommender system users who rate items across disjoint domains face a privacy risk analogous to the one that occurs with statistical database queries. R ecommender systems have become important tools in e-commerce. They combine one user's ratings of products or services with ratings from other users to answer queries such as " Would I like X? " with(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)
We explore the conflict between personalization and privacy that arises from the existence of straddlers in a recommender system. A straddler is a person with eclectic tastes who rates products across several different types or domains. While straddlers enable serendipitous recommendations, information about their existence could be used in conjunction with(More)
Recommender system users who rate items across disjoint domains face a privacy risk analogous to the one that occurs with statistical database queries. R ecommender systems have become important tools in e-commerce. They combine one user's ratings of products or services with ratings from other users to answer queries such as " Would I like X? " with(More)