Abstract: Embodiments of the present disclosure provide for improved management, selection, and provision of network asset data objects. For example, embodiments described throughout are configured to score various network assets corresponding to real-world assets, items, products, and/or the like, for selection and provision associated with one or more particular user profile identifiers. Embodiments are configured to train network asset scoring model(s) based on specific data, such as prioritized network asset data set(s), such that the trained network asset scoring model(s) efficiently generate more accurate, improved scores for network asset data objects. Additional or alternative embodiments are configured to provide data tagged network asset set(s) utilizing specially trained model(s), such as one or more multi-armed bandit models and/or one or more network asset scoring model(s).

Abstract: Embodiments of the present disclosure provide mechanisms for selection of a user survey data object from a set of user data objects, and processing of survey engagement data associated with a selected user survey data object. The user survey data object selected is appropriate for providing associated with a particular user data object, and the survey engagement data received associated therewith enables programmatic selection and use of particular ranking model(s) for use in generating and providing an output ranked item data object set. Example embodiments utilize selected ranking model(s) of a set of ranking models to programmatically generate and output an output ranked item data object set for a particular user profile.

Abstract: Embodiments of the present disclosure provide mechanisms for selection of a user survey data object from a set of user data objects, and processing of survey engagement data associated with a selected user survey data object. The user survey data object selected is appropriate for providing associated with a particular user data object, and the survey engagement data received associated therewith enables programmatic selection and use of particular ranking model(s) for use in generating and providing an output ranked item data object set. Example embodiments utilize selected ranking model(s) of a set of ranking models to programmatically generate and output an output ranked item data object set for a particular user profile.

Abstract: Embodiments of the present disclosure provide for improved management, selection, and provision of network asset data objects. For example, embodiments described throughout are configured to score various network assets corresponding to real-world assets, items, products, and/or the like, for selection and provision associated with one or more particular user profile identifiers. Embodiments are configured to train network asset scoring model(s) based on specific data, such as prioritized network asset data set(s), such that the trained network asset scoring model(s) efficiently generate more accurate, improved scores for network asset data objects. Additional or alternative embodiments are configured to provide data tagged network asset set(s) utilizing specially trained model(s), such as one or more multi-armed bandit models and/or one or more network asset scoring model(s).

Abstract: Embodiments of the present disclosure provide mechanisms for selection of a user survey data object from a set of user data objects, and processing of survey engagement data associated with a selected user survey data object. The user survey data object selected is appropriate for providing associated with a particular user data object, and the survey engagement data received associated therewith enables programmatic selection and use of particular ranking model(s) for use in generating and providing an output ranked item data object set. Example embodiments utilize selected ranking model(s) of a set of ranking models to programmatically generate and output an output ranked item data object set for a particular user profile.

Abstract: Embodiments are provided that facilitate training and utilization of specially configured multi-armed bandit model(s). Such embodiments configure multi-armed bandit model(s) to provide selected network asset scoring model(s) from a network asset scoring model set and/or a selected data tagged network asset set from a set of data tagged network asset sets. Such selections are performable for a particular user profile, for example to perform accurate and/or efficient process(es) for provision of network asset data object(s) to the user profile(s). Some embodiments access enterprise network interaction data, access a set of data tagged network asset sets, train a multi-armed bandit model to provide a selected network asset scoring model and a selected data tagged network asset set, and provides one or more network asset data objects based on the selected network asset scoring model and the selected data tagged network asset set.

Abstract: A system for determining retention risk comprises a grouper, a filter, a normalizer, a feature vector extractor, a model builder, and a predictor. The grouper is for determining a set of time series of transactions where each is associated with one employee. The filter is for filtering the set of time series of transactions based on an employee transition characteristic to determine a subset of time series. The normalizer is for determining a model set of time series by normalizing the subset of time series. The feature vector extractor is for determining a set of feature vectors determined from a time series of the model set of time series. The model builder is for determining one or more models based at least in part on the set of feature vectors. The predictor is for predicting retention risk for a given employee using the one or more models.

Abstract: A system for identifying address components includes an interface and a processor. The interface is to receive an address for parsing. The processor is to determine a matching model of a set of models based at least in part on a matching probability for each model for a tokenized address, which is based on the address for parsing, and associate each component of the tokenized address with an identifier based at least in part on the matching model, wherein each component of the set of components is associated with an identifier, and wherein probabilities of each component of the set of components are determined using training addresses.

Abstract: Techniques disclosed herein describe inferring user interests based on metadata of a plurality of multimedia objects captured by a plurality of users. An analysis tool receives, for each user, metadata describing each multimedia object in the plurality of objects associated with that user. Each multimedia object includes one or more attributes imputed to that object based on the metadata. The analysis tool identifies one or more concepts from the one or more attributes. The analysis tool determines from the one or more concepts, a hierarchical relationship between a first concept and at least a second concept.

Abstract: Techniques are disclosed herein for learning latent interests based on metadata of one or more images. An analysis tool evaluates metadata associated with each digital multimedia object against a knowledge graph, where the knowledge graph is built from data including information external to each of the digital multimedia objects and where the knowledge graph provides a plurality of attributes. The analysis tool associates one or more of the plurality of attributes with each of the digital multimedia objects. Each of the attributes correlates with a time and a location described in the metadata of that object. The analysis tool identifies one or more concepts from attributes associated to each of the objects and maps each of the plurality of attributes to the one or more concepts.

Abstract: Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may determine a score for plays of the streaming media title given the search by multiplying a number of times plays of the media title occur after the query is entered by the number of times any play occurs, and dividing by a product of the number of times plays of the media title occur after any query is entered and the number of times plays of any media title occur after the query is entered.

Abstract: Techniques are disclosed herein for learning latent interests based on metadata of one or more images. An analysis tool associates one or more attributes with each of the objects based on a time and a location described in the metadata of that object. Each of the attributes describes one of a plurality of locations or an event scheduled to occur at one or more of the plurality of locations. The analysis tool identifies one or more concepts from a distribution of the one or more attributes to each of the objects. Each of the one or more concepts includes at least a first attribute in the distribution that co-occurs with a second attribute in the distribution.

Abstract: Techniques disclosed herein describe inferring user interests based on metadata of a plurality of multimedia objects captured by a plurality of users. An analysis tool receives, for each of the users, metadata describing each multimedia object in the plurality of objects associated with that user. Each multimedia object includes one or more attributes imputed to that object based on the metadata. The analysis tool identifies one or more concepts from the one or more attributes. Each concept includes at least a first attribute that co-occurs with a second attribute imputed to a first multimedia object. The analysis tool associates a first one of the plurality of users with at least one of the concepts based on the attributes imputed to multimedia objects associated with the first one of the plurality of users.

Abstract: A system for identifying address components includes an interface and a processor. The interface is to receive an address for parsing. The processor is to determine a matching model of a set of models based at least in part on a matching probability for each model for a tokenized address, which is based on the address for parsing, and associate each component of the tokenized address with an identifier based at least in part on the matching model, wherein each component of the set of components is associated with an identifier, and wherein probabilities of each component of the set of components are determined using training addresses.

Abstract: A system for identifying address components includes a training address interface, a training address probability processor, a parsing address interface, and a processor. The training address interface is to receive training addresses. The training addresses are a set of components with corresponding identifiers. The training address probability processor is to determine probabilities of each component of the training addresses being associated with each identifier. The parsing address interface to receive an address for parsing. The processor is to determine a matching model of a set of models based at least in part on a matching probability for each model for a tokenized address, which is based on the address for parsing, and associate each component of the tokenized address with an identifier based at least in part on the matching model.

Abstract: Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may determine a score for plays of the streaming media title given the search by multiplying a number of times plays of the media title occur after the query is entered by the number of times any play occurs, and dividing by a product of the number of times plays of the media title occur after any query is entered and the number of times plays of any media title occur after the query is entered.

Abstract: Techniques are disclosed herein for learning latent interests based on metadata of one or more images. An analysis tool associates one or more attributes with each of the objects based on a time and a location described in the metadata of that object. Each of the attributes describes one of a plurality of locations or an event scheduled to occur at one or more of the plurality of locations. The analysis tool identifies one or more concepts from a distribution of the one or more attributes to each of the objects. Each of the one or more concepts includes at least a first attribute in the distribution that co-occurs with a second attribute in the distribution.

Abstract: Techniques are disclosed herein for imputing attributes to multimedia content (e.g., an image or a video) based on metadata of the multimedia content. An analysis tool of a multimedia service platform evaluates content metadata to identify at least a time and a first location at which the content was captured. The time and first location is correlated with a knowledge graph. The knowledge graph stores information describing a plurality of locations, including the first location. The knowledge graph also stores information describing events scheduled to occur at one or more of the plurality of locations. The analysis tool identifies, from the correlated information, one or more of the attributes of the first location to impute to the content. Thereafter, the analysis tool imputes the identified attributes to the content.

Abstract: Techniques disclosed herein describe inferring user interests based on metadata of a plurality of multimedia objects captured by a plurality of users. An analysis tool receives, for each of the users, metadata describing each multimedia object in the plurality of objects associated with that user. Each multimedia object includes one or more attributes imputed to that object based on the metadata. The analysis tool identifies one or more concepts from the one or more attributes. Each concept includes at least a first attribute that co-occurs with a second attribute imputed to a first multimedia object.

Abstract: Techniques disclosed herein describe identifying one or more products to recommend to a plurality of users based on metadata of digital multimedia files. A product feed extractor extracts a product feed. The product feed lists one or more items. The product feed extractor identifies, for each item in the product feed, one or more attributes describing the item. Each item is mapped to concepts of an interest taxonomy based on the identified one or more attributes for the item. One or more users are associated with each concept in the interest taxonomy based on the metadata of the digital multimedia files. Each item is associated to one or more of the users based on the mapping.