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Content-based artwork recommendation: integrating painting metadata with neural and manually-engineered visual features

机译：基于内容的艺术品推荐：将绘画元数据与神经和手动工程的视觉功能集成

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摘要

Recommender Systems help us deal with information overload by suggesting relevant items based on our personal preferences. Although there is a large body of research in areas such as movies or music, artwork recommendation has received comparatively little attention, despite the continuous growth of the artwork market. Most previous research has relied on ratings and metadata, and a few recent works have exploited visual features extracted with deep neural networks (DNN) to recommend digital art. In this work, we contribute to the area of content-based artwork recommendation of physical paintings by studying the impact of the aforementioned features (artwork metadata, neural visual features), as well as manually-engineered visual features, such as naturalness, brightness and contrast. We implement and evaluate our method using transactional data from UGallery.com, an online artwork store. Our results show that artwork recommendations based on a hybrid combination of artist preference, curated attributes, deep neural visual features and manually-engineered visual features produce the best performance. Moreover, we discuss the trade-off between automatically obtained DNN features and manually-engineered visual features for the purpose of explainability, as well as the impact of user profile size on predictions. Our research informs the development of next-generation content-based artwork recommenders which rely on different types of data, from text to multimedia.
机译：推荐系统通过根据我们的个人喜好提出相关项目，帮助我们处理信息过载。虽然在电影或音乐等领域有大量的研究，但艺术品推荐在艺术品市场持续增长的情况下，关注相对较少。最先前的研究依赖于评级和元数据，最近的一些作品利用深度神经网络（DNN）提取的视觉特征来推荐数字艺术。在这项工作中，我们通过研究上述特征的影响（图稿元数据，神经视觉特征）以及手动亮度，亮度和手动的视觉功能，为物理绘画的内容的艺术推荐领域有助于物理绘画的建议。对比。我们使用来自Ugallery.com的交易数据来实现和评估我们的在线艺术品商店。我们的研究结果表明，根据艺术家偏好，策划属性，深神经视觉特征和手动视觉功能的杂交组合，提出了基于杂交组合，产生了最佳性能。此外，我们讨论自动获得的DNN特征和手动工程的视觉功能之间的权衡，以便可解释性，以及用户简档大小对预测的影响。我们的研究通知了依赖于不同类型的数据的下一代内容的艺术作品推荐者，从文本到多媒体。

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