User implicit feedback plays an important role in recommender systems. However, finding implicit features is a tedious task. This paper aims to identify users' preferences through implicit behavioural signals for image recommendation based on the Information Scent Model of Information Foraging Theory. In the first part, we hypothesise that the users' perception is improved with visual cues in the images as behavioural signals that provide users' information scent during information seeking. We designed a content-based image recommendation system to explore which image attributes (i.e., visual cues or bookmarks) help users find their desired image. We found that users prefer recommendations predicated by visual cues and therefore consider the visual cues as good information scent for their information seeking. In the second part, we investigated if visual cues in the images together with the images itself can be better perceived by the users than each of them on its own. We evaluated the information scent artifacts in image recommendation on the Pinterest image collection and the WikiArt dataset. We find our proposed image recommendation system supports the implicit signals through Information Foraging explanation of the information scent model.

中文翻译：

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用于增强基于内容的图像推荐中的隐式反馈的信息搜索

用户隐式反馈在推荐系统中起着重要作用。然而，寻找隐含特征是一项乏味的任务。本文旨在基于信息觅食理论的信息气味模型，通过隐式行为信号识别用户偏好进行图像推荐。在第一部分，我们假设用户的感知通过图像中的视觉线索作为行为信号来提高用户的感知能力，这些行为信号在信息搜索过程中为用户提供了信息嗅觉。我们设计了一个基于内容的图像推荐系统来探索哪些图像属性（即视觉提示或书签）帮助用户找到他们想要的图像。我们发现用户更喜欢由视觉线索预测的推荐，因此将视觉线索视为他们寻找信息的良好信息气味。在第二部分，我们调查了图像中的视觉线索与图像本身是否比用户更能感知图像本身。我们在 Pinterest 图像集和 WikiArt 数据集上评估了图像推荐中的信息气味伪影。我们发现我们提出的图像推荐系统通过信息气味模型的信息搜索解释支持隐式信号。