Abstract: Digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. The plurality of digital images each capture the object for inclusion as part of generating digital content, e.g., a webpage, a thumbnail to represent a digital video, and so on. In one example, digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. As a result, the service provider system may select a digital image of an object from a plurality of digital images of the object that has an increased likelihood of achieving a desired outcome and may address the multitude of different ways in which an object may be presented to a user.

Abstract: Digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. The plurality of digital images each capture the object for inclusion as part of generating digital content, e.g., a webpage, a thumbnail to represent a digital video, and so on. In one example, digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. As a result, the service provider system may select a digital image of an object from a plurality of digital images of the object that has an increased likelihood of achieving a desired outcome and may address the multitude of different ways in which an object may be presented to a user.

Abstract: Digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. The plurality of digital images each capture the object for inclusion as part of generating digital content, e.g., a webpage, a thumbnail to represent a digital video, and so on. In one example, digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. As a result, the service provider system may select a digital image of an object from a plurality of digital images of the object that has an increased likelihood of achieving a desired outcome and may address the multitude of different ways in which an object may be presented to a user.

Abstract: Digital content text processing techniques are described. In one example, a text corpus is extracted from digital content and text corpus keywords are identified that are included in the text corpus. A plurality of clusters is formed based on the text corpus keywords. Cluster scores are generated for reviews that define a probability the review belongs to a respective cluster, e.g., based on review keywords extracted from the reviews. Sentiment values and sentiment weights are also generated. The sentiment values describe a sentiment that each of the reviews exhibits towards a respective cluster, e.g., a type of sentiment such as positive, neutral, or negative. The sentiment weights describe an amount of weight to be applied for each sentiment with respect to that cluster. The service provider system then generates ranking scores based on the cluster scores and the sentiment scores which are used to control output of the reviews.

Abstract: Digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. The plurality of digital images each capture the object for inclusion as part of generating digital content, e.g., a webpage, a thumbnail to represent a digital video, and so on. In one example, digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. As a result, the service provider system may select a digital image of an object from a plurality of digital images of the object that has an increased likelihood of achieving a desired outcome and may address the multitude of different ways in which an object may be presented to a user.

Abstract: Background content suggestion for combination with identified items is described. Initially, a system receives digital visual content depicting an item, e.g., a product for purchase. The system identifies various content items that are available for suggesting as replacement backgrounds for the item and then determines a compatibility of these identified content items with the item depicted in the received digital visual content. In particular, the system determines compatibility based on both a scene compatibility and a color compatibility of the identified content items with the depicted item. Based on a combination of the scene and color compatibility, the system surfaces at least one of the identified content items (e.g., a highest scoring content item) as a suggested replacement background for the depicted item.

Abstract: Background content suggestion for combination with identified items is described. Initially, a system receives digital visual content depicting an item, e.g., a product for purchase. The system identifies various content items that are available for suggesting as replacement backgrounds for the item and then determines a compatibility of these identified content items with the item depicted in the received digital visual content. In particular, the system determines compatibility based on both a scene compatibility and a color compatibility of the identified content items with the depicted item. Based on a combination of the scene and color compatibility, the system surfaces at least one of the identified content items (e.g., a highest scoring content item) as a suggested replacement background for the depicted item.