Abstract: In some examples, a method includes identifying a first listing and an additional listing having one or more characteristics in common, the first listing and the additional listing published on an online marketplace; receiving a request to perform an operation on the first listing and the additional listing; in response to the request, generating a batch of events based on the operation, the batch of events including a first event to be performed on the first listing and a second event to be performed on the additional listing; performing the first event on the first listing and the second event on the additional listing.

Abstract: A system trains a computer model to classify images and to draw bounding boxes around classified objects in the images. The system uses a combination of partially labeled training images and fully labeled training images to train a model, such as a neural network model. The fully labeled training images include a classification label indicating a class of object depicted in the image, and bounding box or coordinate labels indicating a number of objects of the class in the image as well as the location of the objects of the class in the image. The partially labeled training images include a classification label but no indication of where in the image any objects of the class are located. Training the model using both types of training data makes it possible for the model to recognize and locate objects of classes that lack available fully labeled training data.

Abstract: In some examples, a method includes identifying a first listing and an additional listing having one or more characteristics in common, the first listing and the additional listing published on an online marketplace; receiving a request to perform an operation on the first listing and the additional listing; in response to the request, generating a batch of events based on the operation, the batch of events including a first event to be performed on the first listing and a second event to be performed on the additional listing; performing the first event on the first listing and the second event on the additional listing.

Abstract: In some examples, a method includes identifying a first listing and an additional listing having one or more characteristics in common, the first listing and the additional listing published on an online marketplace; receiving a request to perform an operation on the first listing and the additional listing; in response to the request, generating a batch of events based on the operation, the batch of events including a first event to be performed on the first listing and a second event to be performed on the additional listing; performing the first event on the first listing and the second event on the additional listing.

Abstract: A system trains a computer model to classify images and to draw bounding boxes around classified objects in the images. The system uses a combination of partially labeled training images and fully labeled training images to train a model, such as a neural network model. The fully labeled training images include a classification label indicating a class of object depicted in the image, and bounding box or coordinate labels indicating a number of objects of the class in the image as well as the location of the objects of the class in the image. The partially labeled training images include a classification label but no indication of where in the image any objects of the class are located. Training the model using both types of training data makes it possible for the model to recognize and locate objects of classes that lack available fully labeled training data.

Abstract: In some examples, a method includes identifying a first listing and an additional listing having one or more characteristics in common, the first listing and the additional listing published on an online marketplace; receiving a request to perform an operation on the first listing and the additional listing; in response to the request, generating a batch of events based on the operation, the batch of events including a first event to be performed on the first listing and a second event to be performed on the additional listing; performing the first event on the first listing and the second event on the additional listing.

Abstract: In one example, a system comprises one or more processors to execute a business event processing module. The business event processing module is configured to receive a request to perform an operation on a listing published by an online marketplace, identify at least one additional listing having certain characteristics in common with the listing from a plurality of published listings including the listing, and automatically perform the operation on the at least one additional listing. The system includes a last event processor to determine a performance of the operation on a last instance of the at least one additional listing.

Abstract: In one example, a system comprises one or more processors to execute a business event processing module. The business event processing module is configured to receive a request to perform an operation on a listing published by an online marketplace, identify at least one additional listing having certain characteristics in common with the listing from a plurality of published listings including the listing, and automatically perform the operation on the at least one additional listing. The system includes a last event processor to determine a performance of the operation on a last instance of the at least one additional listing.

Abstract: In one example embodiment, a system comprises a processor-implemented event processor accessible over a network; a processor-implemented event producer associated with the event processor and configured to monitor an action or directive of a consumer resource and, in response to a detected action or directive, generate an event and event metadata; a processor-implemented converter associated with the event processor configured to acquire the event metadata and generate a generic event based on the acquired event metadata; and a transporter configured to distribute the generic event to a plurality of consumer resources.

Abstract: User activity data describing how a user interacts with recipes posted on a web page or provided by an application is received. A first set of recommended recipes for the user is generated based on the user activity data. A content model that aligns recipe features extracted from the content of the recipes is built based on content of the recipes. A second set of recommended recipes is generated based on the content model. The first set of recommended recipes and the second set of recommended recipes are merged and transmitted for display to the user.

Abstract: Embodiments infer a total preparation time of a recipe. A recipe including preparation steps describing how to prepare a food item is obtained. A recipe server identifies preparation features in the preparation steps, where the preparation features represent portions of the preparations steps that are correlated with time to prepare the recipe. The recipe server obtains preparation times associated with the preparation features and combines these preparation times to estimate to total preparation time of the recipe. The estimated total preparation time is stored or transmitted to a client device in response to a request for the recipe. The estimated total preparation time is used to filter recipes sent in response to a request for recipes, where the request specifies a criterion based on total preparation time. The estimated total preparation time is used to select recipes for recommendation to a user based on that user's preferences.

Abstract: Embodiments infer a temporal attribute of a recipe. A recipe is obtained that includes recipe content such as preparation steps and ingredients. A recipe server identifies attribute features in the recipe content, where the attribute features are representative of portions of the recipe content that are correlated with temporal attributes. The recipe server determines whether the recipe is associated with a temporal attribute based on the attribute features and obtained attribute parameters corresponding to the attribute features. A temporal attribute determined to be associated with a recipe is transmitted to a client device in response to a request for the recipe. The estimated temporal attribute is used to filter recipes sent in response to a request for recipes, where the request specifies a criterion based on the temporal attribute. The estimated temporal attribute is used to select recipes for recommendation to a user based on a current time.

Abstract: In one example, an apparatus comprises processors to execute a business event processing module. The module is configured to receive a request to perform an operation on a first listing in a set of listings and identify, in the set of listings, additional listings having certain characteristics in common with the first listing. The operation is automatically performed on the additional listings and a performance of the operation on a last instance of the additional listings is determined by a last event processor.

Abstract: Embodiments infer a difficulty attribute of a recipe. A recipe is obtained that includes recipe content such as preparation steps and ingredients. A recipe server identifies attribute features in the recipe content, where the attribute features are representative of portions of the recipe content that are correlated with difficulty attributes. The recipe server determines whether the recipe is associated with a difficulty attribute based on the attribute features and obtained attribute parameters corresponding to the attribute features. A difficulty attribute determined to be associated with a recipe is transmitted to a client device in response to a request for the recipe. The estimated difficulty attribute is used to filter recipes sent in response to a request for recipes, where the request specifies a criterion based on the difficulty attribute. The estimated difficulty attribute is used to select recipes for recommendation to a user based on that user's preferences.

Abstract: In one example embodiment, a system comprises a processor-implemented event processor accessible over a network; a processor-implemented event producer associated with the event processor and configured to monitor an action or directive of a consumer resource and, in response to a detected action or directive, generate an event and event metadata; a processor-implemented converter associated with the event processor configured to acquire the event metadata and generate a generic event based on the acquired event metadata; and a transporter configured to distribute the generic event to a plurality of consumer resources.

Abstract: Recipes are hierarchically clustered into groups based on features of the recipes. Candidate clusters with a threshold number of clustered recipes having at least one feature in common are found by traversing the hierarchy. A plurality of clusters is selected for display to a user from among the candidates based on an objective function that considers the relevancy of the cluster as well as diversity of the clusters. A plurality of recipes within each selected cluster is selected for display to a user from among the recipes within the cluster based on an objective function that considers the quality of the recipe as well as the diversity of the recipes within the cluster. At least one feature that all of the recipes in a respective cluster have in common is used to generate a name for the cluster.

Abstract: Embodiments process a recipe from structured data to extract recipe text and select an image representative of the recipe. Recipes in structured data are retrieved and sequenced into segments to facilitate further processing. A recipe parser generates features corresponding to the segments. These generated features are inputs to a recipe model to classify the segments into components. This recipe model is trained according to classified training recipes. The trained model may then determine classifications for segments of the recipe. The classified recipe text is used to select the representative image for the recipe. To select this image, candidate images for a recipe are retrieved and filtered to remove unacceptable images. Features corresponding to these candidate images are generated and used as inputs to an image model to select the representative image. This image model is trained using representative training images of training recipes.

Abstract: In one example, an apparatus comprises processors to execute a business event processing module. The module is configured to receive a request to perform an operation on a first listing in a set of listings and identify, in the set of listings, additional listings having certain characteristics in common with the first listing. The operation is automatically performed on the additional listings and a performance of the operation on a last instance of the additional listings is determined by a last event processor.

Abstract: In one example embodiment, a system comprises an event processing module accessible over a network; an event producer module associated with the event processing module and configured to monitor an action or directive of a consumer resource and, in response to a detected action or directive, generate an event and event metadata. The system further includes a conversion module associated with the event processing module and configured to acquire the event metadata and generate a generic event based on the acquired event metadata, and a transport module configured to distribute the generic event to a plurality of consumer resources.

Abstract: In one example embodiment, a system comprises an event processing module accessible over a network; an event producer module associated with the event processing module and configured to monitor an action or directive of a consumer resource and, in response to a detected action or directive, generate an event and event metadata. The system further includes a conversion module associated with the event processing module and configured to acquire the event metadata and generate a generic event based on the acquired event metadata, and a transport module configured to distribute the generic event to a plurality of consumer resources.