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Tags help users understand a rich information space, by showing them specific text annotations for each item in the space and enabling them to search by these annotations. Often, however, users may wish to move from one item to other items that are similar overall, but that differ in key characteristics. For example, a user who loves Pulp Fiction might want to see a similar movie, but might be in a mood for a less "dark" movie. This paper introduces Movie Tuner, a novel interface that supports navigation from one item to nearby items along dimensions represented by tags. Movie Tuner is based on a data structure called the tag genome, which is described in separate work. The tag genome encodes each item's relationship to a common set of tags by applying machine learning algorithms to user-contributed content. The present paper discusses our design of Movie Tuner, including algorithms for navigating to new items and for suggesting tags for navigation. We present the results of a 7-week field trial of 2,531 users of Movie Tuner, and of a survey evaluating users' subjective experience.
Vig et al. (Sun,) studied this question.