Abstract
This article introduces the tag genome, a data structure that extends the traditional tagging model to provide enhanced forms of user interaction. Just as a biological genome encodes an organism based on a sequence of genes, the tag genome encodes an item in an information space based on its relationship to a common set of tags. We present a machine learning approach for computing the tag genome, and we evaluate several learning models on a ground truth dataset provided by users. We describe an application of the tag genome called Movie Tuner which enables users to navigate from one item to nearby items along dimensions represented by tags. We present the results of a 7-week field trial of 2,531 users of Movie Tuner and a survey evaluating users’ subjective experience. Finally, we outline the broader space of applications of the tag genome.
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