Rush: repeated recommendations on mobile devices

We present rush as a recommendation-based interaction and visualization technique for repeated item selection from large data sets on mobile touch screen devices. Proposals and choices are intertwined in a continuous finger gesture navigating a two-dimensional canvas of recommended items. This provides users with more flexibility for the resulting selections. Our design is based on a formative user study regarding orientation and occlusion aspects. Subsequently, we implemented a version of rush for music playlist creation. In an experimental evaluation we compared different types of recommendations based on similarity, namely the top 5 most similar items, five random selections from the list of similar items and a hybrid version of the two. Participants had to create playlists using each condition. Our results show that top 5 was too restricting, while random and hybrid suggestions had comparable results.

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