Bibliography


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Title:Art I Don't Like: An Anti-Recommender System for Visual Art
Authors:Sarah Frost, Manu Mathew Thomas, Angus G. Forbes
Type:Paper
Publication:MW2019: MuseWeb 2019
Year:2019
Abstract:

"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 the WikiArt dataset, consisting of 52,000 images of art from 14th- to 20th-century European painters, along with feedback from users. Art I Don’t Like is on the web at http://www.artidontlike.com.

Link:https://mw19.mwconf.org/paper/art-i-dont-like-an-anti-recommender-system-for-visual-art