Toward Explainable Fashion Recommendation
Toward Explainable Fashion Recommendation
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DOI：
10.1109/wacv45572.2020.9093367
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发表时间：
2019-01
期刊：
影响因子：
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通讯作者：
Pongsate Tangseng;Takayuki Okatani
中科院分区：
文献类型：
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作者：
Pongsate Tangseng;Takayuki Okatani
到目前为止，已经进行了许多研究来构建用于推荐时尚物品和服装的系统。虽然他们在各自的任务中取得了良好的表现，他们中的大多数不能解释他们的判断给用户，这损害了他们的有用性。针对可解释的时尚推荐，本研究提出了一个系统，它不仅能够提供一个良好的分数为装备，但也解释了分数提供背后的原因。为此，我们提出了一种方法来量化每个项目的每个功能是如何影响的分数。使用该影响值，我们可以识别哪些项目和哪些功能使服装好或坏。我们用人类可解释的特征的组合来表示每个项目的图像，从而识别最有影响力的项目特征对，对输出分数给出了有用的解释。为了评估这种方法的性能，我们设计了一个实验，可以在没有人类注释的情况下进行;我们替换一套服装中的单个项目特征对，以便分数会降低，然后我们测试所提出的方法是否可以使用上述影响值正确检测替换的项目特征对。实验结果表明，该方法能够准确地检测服装中的不良物品，降低其得分。
Many studies have been conducted so far to build systems for recommending fashion items and outfits. Although they achieve good performances in their respective tasks, most of them cannot explain their judgments to the users, which compromises their usefulness. Toward explainable fashion recommendation, this study proposes a system that is able not only to provide a goodness score for an outfit but also to explain the score by providing reason behind it. For this purpose, we propose a method for quantifying how influential each feature of each item is to the score. Using this influence value, we can identify which item and what feature make the outfit good or bad. We represent the image of each item with a combination of human-interpretable features, and thereby the identification of the most influential item-feature pair gives useful explanation of the output score. To evaluate the performance of this approach, we design an experiment that can be performed without human annotation; we replace a single item-feature pair in an outfit so that the score will decrease, and then we test if the proposed method can detect the replaced item-feature pair correctly using the above influence values. The experimental results show that the proposed method can accurately detect bad items in outfits lowering their scores.