Abstract: A plurality of data items associated with user-generated content is identified. A first subset of data items in the plurality of data items is annotated using a first machine learning (ML) model. The first ML model is trained based on the first plurality of labels generated for the first subset of data items. The first ML model is used to annotate a second subset of data items in the plurality of data items. A second ML model is trained based on a second plurality of labels generated based on the annotating of the second subset of data items.

Abstract: A plurality of data items associated with user-generated content is identified. A first subset of data items in the plurality of data items is annotated using a first ML model. A second ML model is trained based on the first plurality of labels generated for the first subset of data items. A second subset of data items in the plurality of data items is annotated using the second ML model trained. A third ML model is trained based on a second plurality of labels generated for the second subset of data items based on the annotating.

Abstract: In implementation of techniques for personalized module arrangement via machine learning, a system receives user interface modules and interaction data corresponding to one or more interaction sessions. Based on the interaction data and the user interface modules, the system generates one or more user history representations via a machine learning model. The system generates, based on the one or more user history representations, interaction likelihood predictions via the machine learning model, wherein each interaction likelihood prediction corresponds to a likelihood of interaction with at least one of the user interface modules. Based on one or more interaction likelihood predictions above a predefined threshold value, the system generates an arrangement of the user interface modules. The system broadcasts the arrangement of the user interface modules for display.

Abstract: In accordance with techniques for context-driven generation of diverse questions, a machine learning model receives a first target question, a second target question, and a context. Based on the context and one or more words of the first target question, a first decoder of the machine learning model outputs a first representation of candidate words to follow the one or more words of the first target question. Based on the context and one or more words of the second target question, a second decoder of the machine learning model outputs a second representation of candidate words to follow the one or more words of the second target question. The machine learning model is fine-tuned to generate diverse questions for a given context based on a diversity loss that captures a degree of variance between the first representation and the second representation.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosure provides a novel, computerized framework for automatically identifying and recommending socially-engaging photos to their creators for sharing. Execution of the disclosed systems and methods turns a tedious manual chore into an automated, software-driven process.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for automatic image selection for online product catalogs. An image selection system gathers feature data for images of an item included in listings posted to an online marketplace. The image selection system uses the feature data as input in a machine learning model to determine probability scores indicating an estimated probability that each image is suitable to represent the item. The machine learning model is trained based on a set of training images of the item that have been labeled to indicate whether they are suitable to represent the image. The image selection system compares the probability scores and selects an image to represent the item as a stock image based on the comparison.

Abstract: One or more computing devices, systems, and/or methods for generating a set of tips for an entity are provided. For example, users may create user generated content describing an entity, such as a user review for a consumer good, a location, an event, etc. Because a user may be unable to read and digest all of the user reviews for the entity, the user may merely read a few user reviews, and thus miss out on useful information. Accordingly, tip templates, indicative of how tips are linguistically/grammatically constructed, are applied to the user reviews to automatically extract a set of tips for the entity (e.g., “make sure to bring a rain jacket”). The set of tips may be filtered to remove undesirable tips, ranked based upon usefulness, and/or diversified to remove redundant tips. In this way, a set of useful tips may be provided to the user.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for automatic image selection for online product catalogs. An image selection system gathers feature data for images of an item included in listings posted to an online marketplace. The image selection system uses the feature data as input in a machine learning model to determine probability scores indicating an estimated probability that each image is suitable to represent the item. The machine learning model is trained based on a set of training images of the item that have been labeled to indicate whether they are suitable to represent the image. The image selection system compares the probability scores and selects an image to represent the item as a stock image based on the comparison.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosure provides a novel, computerized framework for automatically identifying and recommending socially-engaging photos to their creators for sharing. Execution of the disclosed systems and methods turns a tedious manual chore into an automated, software-driven process.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for automatic image selection for online product catalogs. An image selection system gathers feature data for images of an item included in listings posted to an online marketplace. The image selection system uses the feature data as input in a machine learning model to determine probability scores indicating an estimated probability that each image is suitable to represent the item. The machine learning model is trained based on a set of training images of the item that have been labeled to indicate whether they are suitable to represent the image. The image selection system compares the probability scores and selects an image to represent the item as a stock image based on the comparison.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. A novel, computerized framework for automatically identifying and recommending socially-engaging photos to their creators for sharing is provided. Execution of the disclosed framework turns a tedious manual chore into an automated, software-driven process.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for automatic image selection for online product catalogs. An image selection system gathers feature data for images of an item included in listings posted to an online marketplace. The image selection system uses the feature data as input in a machine learning model to determine probability scores indicating an estimated probability that each image is suitable to represent the item. The machine learning model is trained based on a set of training images of the item that have been labeled to indicate whether they are suitable to represent the image. The image selection system compares the probability scores and selects an image to represent the item as a stock image based on the comparison.

Abstract: One or more computing devices, systems, and/or methods for generating a set of tips for an entity are provided. For example, users may create user generated content describing an entity, such as a user review for a consumer good, a location, an event, etc. Because a user may be unable to read and digest all of the user reviews for the entity, the user may merely read a few user reviews, and thus miss out on useful information. Accordingly, tip templates, indicative of how tips are linguistically/grammatically constructed, are applied to the user reviews to automatically extract a set of tips for the entity (e.g., “make sure to bring a rain jacket”). The set of tips may be filtered to remove undesirable tips, ranked based upon usefulness, and/or diversified to remove redundant tips. In this way, a set of useful tips may be provided to the user.

Abstract: One or more computing devices, systems, and/or methods for generating a set of tips for an entity are provided. For example, users may create user generated content describing an entity, such as a user review for a consumer good, a location, an event, etc. Because a user may be unable to read and digest all of the user reviews for the entity, the user may merely read a few user reviews, and thus miss out on useful information. Accordingly, tip templates, indicative of how tips are linguistically/grammatically constructed, are applied to the user reviews to automatically extract a set of tips for the entity (e.g., “make sure to bring a rain jacket”). The set of tips may be filtered to remove undesirable tips, ranked based upon usefulness, and/or diversified to remove redundant tips. In this way, a set of useful tips may be provided to the user.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosure provides a novel, computerized framework for automatically identifying and recommending socially-engaging photos to their creators for sharing. Execution of the disclosed systems and methods turns a tedious manual chore into an automated, software-driven process.

Abstract: One or more computing devices, systems, and/or methods for generating a set of tips for an entity are provided. For example, users may create user generated content describing an entity, such as a user review for a consumer good, a location, an event, etc. Because a user may be unable to read and digest all of the user reviews for the entity, the user may merely read a few user reviews, and thus miss out on useful information. Accordingly, tip templates, indicative of how tips are linguistically/grammatically constructed, are applied to the user reviews to automatically extract a set of tips for the entity (e.g., “make sure to bring a rain jacket”). The set of tips may be filtered to remove undesirable tips, ranked based upon usefulness, and/or diversified to remove redundant tips. In this way, a set of useful tips may be provided to the user.