Abstract: Systems and methods directed to returning personalized image-based search results are described. In examples, a query including an image may be received, and a personalized item embedding may be generated based on the image and user profile information associated with a user. Further, a plurality of candidate images may be obtained based on the personalized item embedding. The candidate images may then be ranked according to a predicted level of user engagement for a user, and then diversified to ensure visual diversity among the ranked images. A portion of the diversified images may then be returned in response to an image-based search.

Abstract: Aspects of the present disclosure relate to a providing related content recommendations in response to a user search query by supervising the training of pair embeddings using contrastive learning and pairwise co-click signals. The approach combines a two tower model architecture with a cascaded multilayer perceptron model to enable the adoption of variable combinations of input features and more representative learned pair embeddings. The learned embeddings undergo supervised contrastive loss training to generate a related content recommendation model, which is subsequently evaluated using both online and offline metrics. The related content recommendation model can provide results to search queries that improve recommendation quality and increase user engagement, thereby ultimately enhancing long term user experience.

Abstract: A computer-implemented technique performs machine learning that bypasses the traditional design of loss functions. The technique includes receiving plural instances of gradient objective information. Each of the plural instances includes a particular combination of plural gradient elements. The technique produces plural sets of machine-trained parameter values using the plural respective instances of gradient objective information. The technique performs this operation based on the plural instances of gradient objective information as given, without calculating the plural instances of gradient objective information using loss functions. The technique then measures performance of the plural sets of machine-trained parameter values in an application system. Based on the measured performance, the technique provides output information that identifies a particular set of machine-trained parameter values that satisfies a prescribed test.

Abstract: Systems and methods directed to returning personalized image-based search results are described. In examples, a query including an image may be received, and a personalized item embedding may be generated based on the image and user profile information associated with a user. Further, a plurality of candidate images may be obtained based on the personalized item embedding. The candidate images may then be ranked according to a predicted level of user engagement for a user, and then diversified to ensure visual diversity among the ranked images. A portion of the diversified images may then be returned in response to an image-based search.

Abstract: The description relates to diversified hybrid image annotation for annotating images. One implementation includes generating first image annotations for a query image using a retrieval-based image annotation technique. Second image annotations can be generated for the query image using a model-based image annotation technique. The first and second image annotations can be integrated to generate a diversified hybrid image annotation result for the query image.

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today.

Abstract: A computer-implemented technique is described herein for generating query results based on both an image and an instance of text submitted by a user. The technique allows a user to more precisely express his or her search intent compared to the case in which a user submits text or an image by itself. This, in turn, enables the user to quickly and efficiently identify relevant search results. In a text-based retrieval path, the technique supplements the text submitted by the user with insight extracted from the input image, and then conducts a text-based search. In an image-based retrieval path, the technique uses insight extracted from the input text to guide the manner in which it processes the input image. In another implementation, the technique generates query results based on an image submitted by the user together with information provided by some other mode of expression besides text.

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today.

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today.

Abstract: The description relates to diversified hybrid image annotation for annotating images. One implementation includes generating first image annotations for a query image using a retrieval-based image annotation technique. Second image annotations can be generated for the query image using a model-based image annotation technique. The first and second image annotations can be integrated to generate a diversified hybrid image annotation result for the query image.

Abstract: Non-limiting examples described herein relate to ensemble model processing for image recognition that improves precision and recall for image recognition processing as compared with existing solutions. An exemplary ensemble model is configured enhance image recognition processing through aggregate data modeling processing that evaluates image recognition prediction results obtained through processing that comprises: nearest neighbor visual search analysis, categorical image classification analysis and/or categorical instance retrieval analysis. An exemplary ensemble model is scalable, where new segments/categories can be bootstrapped to build deeper learning models and achieve high precision image recognition, while the cost of implementation (including from a bandwidth and resource standpoint) is lower than what is currently available across the industry today.