Deep Style Match for Complementary Recommendation
Deep Style Match for Complementary Recommendation
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发表时间：
2017-08
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通讯作者：
Kui Zhao;Xia Hu;Jiajun Bu;Can Wang
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作者：
Kui Zhao;Xia Hu;Jiajun Bu;Can Wang
人们根据物品的属性发展出一种样式兼容性的常识。我们试图自动回答诸如“这件衬衫配那条牛仔裤好吗？”为了回答这些问题，本文试图建立人类风格兼容性的模型。我们的方法的基本假设是，在线商店中产品的大多数重要属性都包含在其标题描述中。因此，从这些描述中学习风格兼容性是可行的。我们设计了一个Siamese卷积神经网络架构，并为其提供项目的标题对，这些标题对要么兼容，要么不兼容。这些对将从符号词的原始空间映射到嵌入的样式空间中。我们的方法只使用单词作为输入，预处理较少，不需要费力和昂贵的特征工程。
Humans develop a common sense of style compatibility between items based on their attributes. We seek to automatically answer questions like "Does this shirt go well with that pair of jeans?" In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper. The basic assumption of our approach is that most of the important attributes for a product in an online store are included in its title description. Therefore it is feasible to learn style compatibility from these descriptions. We design a Siamese Convolutional Neural Network architecture and feed it with title pairs of items, which are either compatible or incompatible. Those pairs will be mapped from the original space of symbolic words into some embedded style space. Our approach takes only words as the input with few preprocessing and there is no laborious and expensive feature engineering.