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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人工设计的视觉功能集成在一起

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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建议相关项目，帮助我们应对信息过多的情况。尽管在电影或音乐等领域有大量的研究，但艺术品市场尽管持续增长，但艺术品推荐却很少受到关注。以前的大多数研究都依赖于评级和元数据，而最近的一些作品则利用深度神经网络（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（艺术品元数据，神经视觉特征）以及人工设计的视觉特征（例如自然度，亮度和亮度）的影响，为实物绘画的基于内容的艺术品推荐领域做出了贡献对比。我们使用在线艺术品商店UGallery.com的交易数据来实施和评估我们的方法。我们的结果表明，基于艺术家偏好，策展属性，深层神经视觉特征和人工设计的视觉特征的混合组合提出的艺术品推荐产生了最佳性能。此外，出于可解释性的目的，我们讨论了自动获得的DNN功能与手动设计的视觉功能之间的权衡，以及用户个人资料大小对预测的影响。我们的研究为下一代基于内容的艺术品推荐器的开发提供了帮助，这些推荐器依赖于从文本到多媒体的不同类型的数据。

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