用用户拍的照片解释推荐理由，让推荐更可信。
Towards Explainable Personalized Recommendations by Learning from Users' Photos
- 基于用户上传的图片预测其对商品的偏好理由。
- 在六座城市的餐厅数据上验证，能有效捕捉用户关注点。
- 适合想提升推荐可解释性与用户信任度的平台使用。
解释复杂系统（如推荐系统）的输出结果，对用户和企业都越来越重要。本文探索将个性化解释本身作为推荐任务来学习。许多在线服务允许用户上传照片并评分商品，我们假设用户拍照是为了强化或证明其对商品的看法。因此，我们尝试预测用户会对某个商品拍摄哪张照片，因为这张图是其最能说服自己的理由。由此，推荐系统不仅能给出结果，还能提供合理解释，增强可靠性。此外，一旦建立预测模型，便可估算用户偏好的图片分布，帮助企业了解客户关注产品哪些方面。本文提出一个形式化框架，用于估计给定用户-照片对的作者归属概率。实验基于来自TripAdvisor的六座城市餐厅评论数据集（含照片），验证了该方法的有效性。
原文摘要 · Abstract (English)
Explaining the output of a complex system, such as a Recommender System (RS), is becoming of utmost importance for both users and companies. In this paper we explore the idea that personalized explanations can be learned as recommendation themselves. There are plenty of online services where users can upload some photos, in addition to rating items. We assume that users take these photos to reinforce or justify their opinions about the items. For this reason we try to predict what photo a user would take of an item, because that image is the argument that can best convince her of the qualities of the item. In this sense, an RS can explain its results and, therefore, increase its reliability. Furthermore, once we have a model to predict attractive images for users, we can estimate their distribution. Thus, the companies acquire a vivid knowledge about the aspects that the clients highlight of their products. The paper includes a formal framework that estimates the authorship probability for a given pair (user, photo). To illustrate the proposal, we use data gathered from TripAdvisor containing the reviews (with photos) of restaurants in six cities of different sizes.
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