基于图像的机器学习技术将数十亿的电子商务产品分为数千个类别

{"type":"doc","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"在Criteo(中文名科稻,一家在纳斯达克上市的全球性的效果营销科技公司)的通用目录(Universal Catalog)团队,我们每天与数十亿产品打交道,来创建全球最大的电子商务目录之一:250多亿种产品。这些产品,由我们的电子商务合作伙伴提供,具有不同的数据字段,我们使用这些数据字段来创建"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"补充(enrichments)"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":":新的产品字段标准化给定数据,并由Criteo全球团队重复使用。一个重要的补充是按类别对产品进行分类。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":" "}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"italic"},{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"在本文,我将描述我如何解决通过使用电子商务产品的主图像将其划分为数千个类别的难题。我选择了一种在GPU上使用TensorFlow的深度学习算法,使用了一个包含数百万图像的标注过的数据集。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":" "}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"在Criteo,我们有数万个电子商务合作伙伴,他们为我们提供了总计250多亿种产品的目录。这些产品通过我们的在线广告推荐给互联网用户,确保这些互联网用户与我们的电子商务合作伙伴的广告活动相关。为了保证推荐的质量,我们"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"需要标准化这组异构目录"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"。特别是,每个产品都应归入其电子商务类别。而且无论原始目录如何,"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"所有这些产品的这组电子商务类别都应该是相同的"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"。然而,每个电子商务合作伙伴都为我们提供了每种产品的类别,但不是用"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"通用的参考"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"。在Criteo,我们使用了一种由谷歌提供的被电子商务生态系统广泛使用的分类:"},{"type":"link","attrs":{"href":"https:\/\/support.google.com\/merchants\/answer\/6324436?hl=en","title":null,"type":null},"content":[{"type":"text","text":"谷歌产品分类法(Google Product Taxonomy)"}],"marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}]},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"。它仅用于"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"零售产品"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":",而不同类型的产品使用了其它技术。然后,我们用这个分类法将零售产品重新分类。所有可能的零售产品都分类到这个树型结构中。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/infoq\/8d\/8dc23cdebb139557e4bfe1a06347e44a.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":"center","origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#757575","name":"user"}}],"text":"产品类别参考"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"上图是上述谷歌产品分类法("},{"type":"link","attrs":{"href":"https:\/\/support.google.com\/merchants\/answer\/6324436?hl=en","title":null,"type":null},"content":[{"type":"text","text":"Google Product Taxonomy"}],"marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}]},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":")的一个示例。这是一个树型结构,我们截断到第4级或更上层的叶子类别。我们构建了一个"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"机器学习模型"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"来"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"预测"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"每个产品的"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"所有类别"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":",直到它的"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"叶子类别"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"。但是我们只使用预测的叶子类别,其它预测的父类别用于调试。预测到的叶子类别信息能够检索到"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"根类别"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"的路径。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":" "}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"重新计算出的叶子类别称为“"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"通用类别"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"”。如果一个目录已经使用了这种分类法,我们仍然会重新对其进行计算。一旦重新计算,所有这些目录就构成了一个独特的大型电子商务目录(超过250亿种产品),称为“"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"通用目录"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"”,在Criteo的整个生态系统中使用。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/infoq\/25\/259c8d1d675a1281e90db5656f6f18cc.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":"center","origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#757575","name":"user"}}],"text":"Criteo通用目录"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"这种产品分类问题存在于Criteo环境中,但只要你希望合并不同来源的目录(其目录对于类别字段的值没有一种通用的格式),这"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"在电子商务生态系统中是一个普遍存在的问题"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":" "}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"目前,在生产环境中,为了计算每种产品的通用类别,我们已经根据这些产品的文本特性构建了一个机器学习模型。但没有利用到"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"产品图像"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"。我们能否仅使用这些产品图像来预测“通用类别”,同时保持良好的性能?"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"对于这个机器学习问题,特征(feature)是唯一的主图像,标签(label)是预测产品所属的类别"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/infoq\/0d\/0d66f4c7a89d9b29a5c36a6c0a46b305.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":"center","origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#757575","name":"user"}}],"text":"使用产品图像来预测通用类别"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user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1.15在训练\/验证数据集上训练每个模型,然后在多个GPU上使用TensorFlow 2+进行训练"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":5,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"使用准确性和Criteo业务价值来评估数据集上的每个模型,然后使用比较网格和多维混淆矩阵分析它们的得分"}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":" "}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"让我们具体看看每一步吧!"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"使用分布式计算创建一个数据集"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/infoq\/ae\/aeacea6862ac8bc89921ddc199fe487b.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"在Criteo,我们已经积累了"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"数百万种标注有它们的目标类别的产品"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"。这很棒,但是这些标注过的数据集"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"只有到图像的链接,而没有图像本身"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"!这意味着在这里提到的工作之前——合作伙伴的产品"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"没有可用的图像数据集"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"!"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":" "}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"为了检查有多少图像不可用或响应太慢,并使用已经可用的标注过的数据,我首先用2个多小时下载了10000个产品的小样本。我发现有1%的图像链接无效或者丢失,约12%的图像无法下载(图像不再存在,链接仍然存在但被重定向到首页,等等)。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/infoq\/b9\/b9dd9491256d8dff87a3dca0f5c34228.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":"center","origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#757575","name":"user"}}],"text":"创建数据集的Spark 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TFRecordDatasets(TensorFlow数据集)存储到HDFS上。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/infoq\/27\/27c8eee967969b46147a0237a4b2fb55.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":"center","origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#757575","name":"user"}}],"text":"数据集拆分"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"一旦下载并存储了所有东西,我们就可以使用70-15-15的比例来对数据进行拆分,分别是训练(大约2500万个产品)、验证(大约500万个产品)、测试(大约500个产品)。我们的数据集构建好之后,我们就可以深入到深度学习问题本身,选择使用哪些模型。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"学习最先进的方法"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"在Criteo,我们使用TensorFlow 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1.15中没有的更多的最新模型。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":" "}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"然后我推动将TensorFlow 1.15更改为TensorFlow 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护发素”而真实类别为“皮肤护理”的产品为15%(或预测“婴幼儿上衣”而真实类别为“婴幼儿外套”的产品为10%),这些产品在视觉上确实彼此相似。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"深度总结"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}},{"type":"strong"}],"text":"在500多万张图像的测试集上,准确率超过90%"},{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":",显然接下来的步骤之一就是将该项目与生产中已经使用的NLP方法集成,来构建通用目录。例如,可以使用多模式学习 [4] 或将这两种方法结合使用,例如使用集成学习 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[7])绝对是一种选择,但创建我们自己的模型也开始成为一条有吸引力的路线!"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":" "}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"感谢阅读!"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":" "}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"color","attrs":{"color":"#494949","name":"user"}}],"text":"我要感谢"},{"type":"link","attrs":{"href":"https:\/\/medium.com\/u\/3781afe97e98?source=post_page-----6e029fc8d496--------------------------------","title":null,"type":null},"content":[{"type":"text","text":"Romain 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