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DNNs are able to automatically learn\nfeature interactions; however, they generate all the interactions implicitly,\nand are not necessarily efficient in learning all types of cross features. In\nthis paper, we propose the Deep & Cross Network (DCN) which keeps the benefits\nof a DNN model, and beyond that, it introduces a novel cross network that is\nmore efficient in learning certain bounded-degree feature interactions. In\nparticular, DCN explicitly applies feature crossing at each layer, requires no\nmanual feature engineering, and adds negligible extra complexity to the DNN\nmodel. 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