Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: Navigation and reward techniques involving physical goods and services are described. In one example, digital content is configured to aid navigation of a user between different physical goods or services. This navigation includes user specified good or services as well as recommended goods or services that are not specified by the user. In another example, digital content is provided as part of a reward system. In return for permitting access to user data, the user is provided with rewards that are based on this monitored interaction. In this way, an owner of the store may gain detailed knowledge which may be used to increase likelihood of offering goods or services of interest to the user. In return, the user is provided with rewards to permit access to this detailed knowledge.

Abstract: Navigation and reward techniques involving physical goods and services are described. In one example, digital content is configured to aid navigation of a user between different physical goods or services. This navigation includes user specified good or services as well as recommended goods or services that are not specified by the user. In another example, digital content is provided as part of a reward system. In return for permitting access to user data, the user is provided with rewards that are based on this monitored interaction. In this way, an owner of the store may gain detailed knowledge which may be used to increase likelihood of offering goods or services of interest to the user. In return, the user is provided with rewards to permit access to this detailed knowledge.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: In implementations of determining user segmentation based on a photo library, a device maintains digital images in the photo library, as well as metadata associated with the digital images. The device includes a segmentation module implemented to determine characteristics about a user of the device by analysis of the metadata of the digital images. The segmentation module can determine a segmentation based on the characteristics determined about the user. The segmentation includes one or more segments that each represent a generalized aspect of the user, where a generalized aspect is attributable to multiple people and anonymity of the user is maintained. The segmentation module can associate an anonymous identifier with the segmentation effective to maintain the anonymity of the user and privacy of the metadata. The segmentation and the anonymous identifier can then be communicated to a marketing system that generates personalized marketing messages based on the segmentation.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: In implementations of determining user segmentation based on a photo library, a device maintains digital images in the photo library, as well as metadata associated with the digital images. The device includes a segmentation module implemented to determine characteristics about a user of the device by analysis of the metadata of the digital images. The segmentation module can determine a segmentation based on the characteristics determined about the user. The segmentation includes one or more segments that each represent a generalized aspect of the user, where a generalized aspect is attributable to multiple people and anonymity of the user is maintained. The segmentation module can associate an anonymous identifier with the segmentation effective to maintain the anonymity of the user and privacy of the metadata. The segmentation and the anonymous identifier can then be communicated to a marketing system that generates personalized marketing messages based on the segmentation.

Abstract: Certain embodiments involve determining visual context associated with user behavior and associating the visual context with the user behavior. For example, a system captures a portion of a user interface provided to a user at a time of a user action on the user interface. The captured portion can include digital content and the system can detect the digital content. The system can also generate a digital representation of the digital content. The digital representation can indicate the digital and exclude the digital content. The system can determine a visual context associated with the user action based on the generated digital representation. The visual context describes the digital content displayed via the user interface at the time of the user action. The system can also determine subsequent digital content to output to the user to create a subsequent visual context to encourage a particular user behavior.

Abstract: Methods and systems are provided for providing help regarding an in-app issue with an application using a contextual help system. Help can be provided automatically based on an analysis of in-app events leading up to the indication for help with the in-app issue. Automatic help can be provided in the form of help content determined to be likely to resolve the in-app issue. When no help content is determined to be likely to resolve the issue, the in-app events can be converted into a human-readable form and sent to a live help desk for use in resolving the issue. In this way, in-app issues can be resolved based on evaluations of the in-app event leading up the occurrence of the issue.

Abstract: In an example embodiment, user interactions with a software component may be tracked in an efficient manner. Specifically, an analytics tracking request triggered by user interaction with a software component is received. Then a value assigned to the user is retrieved. It is then determined if the value assigned to the user exceeds a value threshold assigned to the analytics tracking request. Based on a comparison between the value assigned to the user and the threshold value, an analytics tracking function associated with the analytics tracking request is launched.

Abstract: Facilitating analysis of user interface gesture patterns is described. In example implementations, a computing device acquires data that describes a user interface gesture pattern, such as finger movements on a touchscreen, in terms of pixels of a visual display. A repetitive arrangement of polygons, such as a grid of rectangles, is logically overlaid on the visual display. The computing device transforms the pixel-based data into polygon-based data that represents the gesture pattern in terms of polygons traversed by the gesture pattern. The computing device also converts the polygon-based data into text-based data such that the gesture pattern is represented by textual characters, such as a text string. The text string can include, for instance, a list of polygons traversed by the gesture pattern. The text-based data is forwarded to a service that can efficiently analyze relationships that may exist among multiple gesture patterns across multiple devices or end users.

Abstract: Certain embodiments involve determining visual context associated with user behavior and associating the visual context with the user behavior. For example, a system captures a portion of a user interface provided to a user at a time of a user action on the user interface. The captured portion can include digital content and the system can detect the digital content. The system can also generate a digital representation of the digital content. The digital representation can indicate the digital and exclude the digital content. The system can determine a visual context associated with the user action based on the generated digital representation. The visual context describes the digital content displayed via the user interface at the time of the user action. The system can also determine subsequent digital content to output to the user to create a subsequent visual context to encourage a particular user behavior.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: Techniques and system are described to control output of digital marketing content with respect to a digital video that address the added complexities of digital video over other types of digital content, such as webpages. In one example, the techniques and systems are configured to control a time, at which, digital marketing content is to be output with respect to the digital video, e.g., by selecting a commercial break or output as a banner ad in conjunction with the video.

Abstract: Techniques and system are described to control output of digital marketing content with respect to digital content. This is achieved by leveraging additional insight that may be gained from external service systems that describe the digital content, e.g., social network systems, digital content review systems, and so forth. In one example, the techniques and systems are configured to collect social network data that describes social network communications communicated via a social network system. Natural language processing techniques are then performed as part of machine learning to detect interest of a user population associated with the social network communications.

Abstract: Facilitating location-aware analysis is described. In some embodiments, a database building module is configured to build a point of interest (POI) database based on a tree data structure that includes multiple nodes respectively corresponding to multiple areas. The database building module includes a content node processing module that inserts an entry in the POI database having a content field populated by a POI descriptor included with a content node. The database building module also includes a reference node processing module that inserts an entry having multiple reference fields respectively populated with area indicators corresponding to multiple subnodes of a reference node. In other embodiments, a POI database search module is configured to search a POI database to ascertain multiple POIs with regard to a location of a computing device. The search module searches key fields of reference entries and content entries using an area indicator matching the computing device's location.

Abstract: Facilitating analysis of user interface gesture patterns is described. In example implementations, a computing device acquires data that describes a user interface gesture pattern, such as finger movements on a touchscreen, in terms of pixels of a visual display. A repetitive arrangement of polygons, such as a grid of rectangles, is logically overlaid on the visual display. The computing device transforms the pixel-based data into polygon-based data that represents the gesture pattern in terms of polygons traversed by the gesture pattern. The computing device also converts the polygon-based data into text-based data such that the gesture pattern is represented by textual characters, such as a text string. The text string can include, for instance, a list of polygons traversed by the gesture pattern. The text-based data is forwarded to a service that can efficiently analyze relationships that may exist among multiple gesture patterns across multiple devices or end users.