DemonstrationSarah Frost, University of California, Santa Cruz, United States, Manu Mathew Thomas, University of California, Santa Cruz, United States, Angus G. Forbes, University of California, Santa Cruz, United States
Recommender systems show users products, songs, and political points of view that are similar to content that they have already seen or previously indicated that they like. While these methods are useful for generating sales and clicks, they are not necessarily successful at exposing users to disparate content. Art I Don’t Like is a Web-based interactive art experience that provides personalized content to users and emphasizes the introduction of disparate content. We suggest a new “anti-recommender” system that provides content that is aesthetically related in terms of low-level features, but challenges the implied conceptual frameworks indicated by initial user selections. Furthermore, we demonstrate an application of recommender technologies to visual art in an effort to expose users to a broad range of art genres. We present details of a prototype implementation trained on a subset of WikiArt dataset consisting of 52,000 images of art from 14th-20th century European painters, along with feedback from users. Art I Don’t Like is on the web at http://www.artidontlike.com.
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