Abstract: A call reminder functionality is provided by an arrangement that is operable with a network-enabled communication device in which a reminder to return a phone call can be generated in an automated manner upon the occurrence of a call event. When the call event occurs, an option is displayed on a user interface (“UI”) on the device to create a link between the call and the user's schedule application. If the user selects the option, a callback task is added to the user's task list maintained by the schedule application, and a reminder to make the callback is generated and then displayed on the UI at a future time. The reminder includes pertinent call information and also includes a button that enables the user to place the callback through the UI (i.e., a “one-click call back”).

Abstract: A call reminder functionality is provided by an arrangement that is operable with a network-enabled communication device in which a reminder to return a phone call can be generated in an automated manner upon the occurrence of a call event. When the call event occurs, an option is displayed on a user interface (“UI”) on the device to create a link between the call and the user's schedule application. If the user selects the option, a callback task is added to the user's task list maintained by the schedule application, and a reminder to make the callback is generated and then displayed on the UI at a future time. The reminder includes pertinent call information and also includes a button that enables the user to place the callback through the UI (i.e., a “one-click call back”).

Abstract: A call reminder functionality is provided by an arrangement that is operable with a network-enabled communication device in which a reminder to return a phone call can be generated in an automated manner upon the occurrence of a call event. When the call event occurs, an option is displayed on a user interface (“UI”) on the device to create a link between the call and the user's schedule application. If the user selects the option, a callback task is added to the user's task list maintained by the schedule application, and a reminder to make the callback is generated and then displayed on the UI at a future time. The reminder includes pertinent call information and also includes a button that enables the user to place the callback through the UI (i.e., a “one-click call back”).

Abstract: Disclosed herein is a system and method for generating a user profile for a user based on the way the user interacts with consumable content in an application, such as an electronic book in an e-reader. The actions of the user with regards to the content are monitored and the associated with characteristics of the content. This association is used to build the user's profile of their likes and dislikes which are then provided to a recommender system to provide more relevant and personalize recommendations.

Abstract: A call reminder functionality is provided by an arrangement that is operable with a network-enabled communication device in which a reminder to return a phone call can be generated in an automated manner upon the occurrence of a call event. When the call event occurs, an option is displayed on a user interface (“UI”) on the device to create a link between the call and the user's schedule application. If the user selects the option, a callback task is added to the user's task list maintained by the schedule application, and a reminder to make the callback is generated and then displayed on the UI at a future time. The reminder includes pertinent call information and also includes a button that enables the user to place the callback through the UI (i.e., a “one-click call back”).

Abstract: A call reminder functionality is provided by an arrangement that is operable with a network-enabled communication device in which a reminder to return a phone call can be generated in an automated manner upon the occurrence of a call event. When the call event occurs, an option is displayed on a user interface (“UI”) on the device to create a link between the call and the user's schedule application. If the user selects the option, a callback task is added to the user's task list maintained by the schedule application, and a reminder to make the callback is generated and then displayed on the UI at a future time. The reminder includes pertinent call information and also includes a button that enables the user to place the callback through the UI (i.e., a “one-click call back”).

Abstract: A method includes acts for filtering auto consumption recommendations and auto consumption actions. The method includes receiving from a recommendation system, a recommendation of an asset for consumption. The asset for consumption is evaluated in the context of one or more filter rules regarding auto consumption. The filter rules are configured to filter recommended assets from being consumed when certain criteria are met or to permit recommended assets to be consumed when certain criteria are met. As a result, the method includes identifying one or more constraints on how recommended asset should be consumed. The method further includes filtering consumption of the recommended asset based on the one or more constraints.

Abstract: The subject disclosure relates to a knowledge-driven data quality solution that is based on a rich knowledge base. The data quality solution can provide continuous improvement and can be based on continuous (or on-going) knowledge acquisition. The data quality solution can be built once and can be reused for multiple data quality improvements, which can be for the same data or for similar data. The disclosed aspects are easy to use and focus on productivity and user experience. Further, the disclosed aspects are open and extendible and can be applied to cloud-based reference data (e.g., a third party data source) and/or user generated knowledge. According to some aspects, the disclosed aspects can be integrated with data integration services.

Abstract: Disclosed herein is a system and method for automatically identifying potential malware files or benign files in files that are not known to be malware. Vector distances for select features of the files are compared to vectors both known malware files and benign files. Based on the distance measures a malware score is obtained for the unknown file. If the malware score exceeds a threshold a researcher may be notified of the potential malware, or the file may be automatically classified as malware if the score is significantly high.

Abstract: Disclosed herein is a system and method for generating a user profile for a user from multiple different data sources and providing recommendations to a user that are directed to benefiting the user and not benefiting a particular merchant or service provider. This disparate data is taken from multiple different data sources that are often competitors of one another. From the multiple data sources the same transaction or event can be determined and correlated with each other. This information is then processed to generate the user's profile which is used to generated recommendations for the user based on the user's needs. Recommendations that are presented can be positive or negative recommendations.

Abstract: A method includes acts for filtering auto consumption recommendations and auto consumption actions. The method includes receiving from a recommendation system, a recommendation of an asset for consumption. The asset for consumption is evaluated in the context of one or more filter rules regarding auto consumption. The filter rules are configured to filter recommended assets from being consumed when certain criteria are met or to permit recommended assets to be consumed when certain criteria are met. As a result, the method includes identifying one or more constraints on how recommended asset should be consumed. The method further includes filtering consumption of the recommended asset based on the one or more constraints.

Abstract: One or more web-behavior-augmented recommendation servers augment catalog interaction data between consumers and a catalog of products with collected online web behavior data associated with individual users. The online web behavior data represents online web behavior of the each consumer outside of the catalog of products and can improve the accuracy of product recommendations, which then be presented to an individual consumer via a computer user interface.

Abstract: Disclosed herein is a system and method for a system and method for determining whether the protection level of a protection system is appropriate for the way the user of a computing system is using the device. The protection system monitors the user's activity while they are using the various applications on the device. The protection system identifies an activity record that is the most similar to the user's activity and compares the current protection level with the associated record's protection level. The protection system may change the protection level when the user's protection level and the associated record's protection level are different.

Abstract: Disclosed herein is a system and method for automatically identifying potential malware files or benign files in files that are not known to be malware. Vector distances for select features of the files are compared to vectors both known malware files and benign files. Based on the distance measures a malware score is obtained for the unknown file. If the malware score exceeds a threshold a researcher may be notified of the potential malware, or the file may be automatically classified as malware if the score is significantly high.

Abstract: A call reminder functionality is provided by an arrangement that is operable with a network-enabled communication device in which a reminder to return a phone call can be generated in an automated manner upon the occurrence of a call event. When the call event occurs, an option is displayed on a user interface (“UI”) on the device to create a link between the call and the user's schedule application. If the user selects the option, a callback task is added to the user's task list maintained by the schedule application, and a reminder to make the callback is generated and then displayed on the UI at a future time. The reminder includes pertinent call information and also includes a button that enables the user to place the callback through the UI (i.e., a “one-click call back”).

Abstract: The present invention extends to methods, systems, and computer program products for exploring and selecting data cleansing service providers. Embodiments of the invention permit a user to explore different data cleansing service providers and compare quality results from the different data cleansing service providers. Sample data is mapped to a specified data domain. A list of service providers, for cleansing data for the selected data domain, is provided to a user. The user selects a subset of service providers. The sample data is submitted to the subset of service providers, which return results including allegedly cleansed data. The results are profiled and a comparison of the subset of service providers is presented to the user. The user selects a service provider to use when cleansing further data.

Abstract: Disclosed herein is a system and method for identifying features of items that are more relevant for making recommendations to consumers for content that they may be interested in. The system determines the similarity between items that are recommend and items in the user's history and compares that similarity measure to the similarity measure calculated for a random item on the same features. From this similarity measure the relative impactfullness of a particular feature on a recommendation can be determined.

Abstract: The subject disclosure relates to a knowledge-driven data quality solution that is based on a rich knowledge base. The data quality solution can provide continuous improvement and can be based on continuous (or on-going) knowledge acquisition. The data quality solution can be built once and can be reused for multiple data quality improvements, which can be for the same data or for similar data. The disclosed aspects are easy to use and focus on productivity and user experience. Further, the disclosed aspects are open and extendible and can be applied to cloud-based reference data (e.g., a third party data source) and/or user generated knowledge. According to some aspects, the disclosed aspects can be integrated with data integration services.

Abstract: Disclosed herein is a system and method for generating a user profile for a user based on the way the user interacts with consumable content in an application, such as an electronic book in an e-reader. The actions of the user with regards to the content are monitored and the associated with characteristics of the content. This association is used to build the user's profile of their likes and dislikes which are then provided to a recommender system to provide more relevant and personalize recommendations.

Abstract: Disclosed herein is a system and method for determining that a current user profile in a system should be modified or changed. An audience detection component detects that a characteristic has been detected that does not match at least one characteristic in the current user profile. The audience detection component determines how the profile should be modified or restricted based on the inputs received from the sensors. The modified profile is then provided to a recommender system so that appropriate content may be suggested to the consumers without any further intervention or action required by the user.