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This demonstration presents AMARA, a novel interactive system to help novice art enthusiasts browse online art collections. Currently being developed for the Indianapolis Museum of Art website, the system uses an embedded interactive agent who asks users a series of simple and straightforward multiple-choice questions regarding users' current feelings, preferences, and interests in art. The answers to each question are mapped to social tags, which are then used to retrieve and display relevant artworks the user may find interesting or appealing. Using AMARA, users can easily browse or search through online art collections without having to use a traditional keyword search, which requires extensive knowledge of art terminology or advanced subject expertise. Thus, AMARA was designed to enhance users' enjoyment and engagement with online art collections and assist users in discovering their known and unknown preferences in art.

Social tagging is one of the most popular methods for collecting crowd-sourced information in galleries, libraries, archives, and museums (GLAMs). However, when the number of social tags grows rapidly, using them becomes problematic and, as a result, they are often left as simply big data that cannot be used for practical purposes. To revitalize the use of this crowdsourced information, we propose using social tags to link and cluster artworks based on an experimental study using an online collection at the Gyeonggi Museum of Modern Art (GMoMA). We view social tagging as a folksonomy, where artworks are classified by keywords of the crowd's various interpretations and one artwork can belong to several different categories simultaneously. To leverage this strength of social tags, we used a clustering method called "link communities" to detect overlapping communities in a network of artworks constructed by computing similarities between all artwork pairs. We used this framework to identify semantic relationships and clusters of similar artworks. By comparing the clustering results with curators' manual classification results, we demonstrated the potential of social tagging data for automatically clustering artworks in a way that reflects the dynamic perspectives of crowds.

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