论文标题
在基于内容的图像建议中增强隐式反馈的信息觅食
Information Foraging for Enhancing Implicit Feedback in Content-based Image Recommendation
论文作者
论文摘要
用户隐式反馈在推荐系统中起重要作用。但是，找到隐性功能是一项繁琐的任务。本文旨在通过信息觅食理论的信息气味模型来通过隐式行为信号来确定用户的偏好。在第一部分中，我们假设用户的感知通过图像中的视觉提示作为行为信号来提高，这些信号在寻求信息期间为用户提供了信息。我们设计了一个基于内容的图像建议系统，以探索哪些图像属性（即视觉提示或书签）可帮助用户找到所需的图像。我们发现，用户更喜欢由视觉提示提出的建议，因此将视觉提示视为信息寻求信息的良好信息气味。在第二部分中，我们调查了图像中的视觉提示以及图像本身是否可以比单独观察到的每个人更好地感知。我们评估了Pinterest图像收集和Wikiart数据集中图像建议中的信息气味。我们发现我们提出的图像推荐系统通过信息觅食的说明来支持信息气味模型的隐含信号。
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.
