Abstract: Techniques are disclosed for accurately identifying distinct physical user devices in a cross-device context. An example embodiment applies a multi-phase approach to generate labeled training datasets from a corpus of unlabeled device records. Such labeled training datasets can be used for training machine learning systems to predict the occurrence of device records that have been wrongly (or correctly, as the case may be) attributed to different physical user devices. Such identification of improper attribution can be particularly helpful in web-based analytics. The labeled training datasets include labeled pairs of device records generated using multiple strategies for inferring whether the two device records of a pair of device records represent the same physical user device (or different physical user devices). The labeled pairs of device records can then be used to train classifiers to predict with confidence whether two device records represent or do not represent the same physical user device.

Abstract: Described herein is a framework for classifying social media inputs. In accordance with one aspect of the framework, one or more social media inputs is acquired from one or more social media platforms. The social media inputs are cleaned to remove redundant elements. One or more features are extracted from the cleaned social media inputs. The social media inputs are classified by a trained classifier into predefined categories using the extracted one or more features.

Abstract: Described herein is a machine learning framework for facilitating engagements. In accordance with one aspect of the framework, a machine learning model is trained based on the training data. A recommendation associated with an opportunity record may then be generated using the trained machine learning model. Results of one or more actions performed in response to the recommendation may be collected and fed back to the machine learning model to be used as the training data.

Abstract: A framework for improving data set in an enterprise system for machine learning is provided. In accordance with one aspect, user input of a project update is provided by a user to an enterprise system. A record of the project update is created in the enterprise system based on the user input. The project update provided by the user into the enterprise system is analyzed using a gamification technique. The analysis includes quantifying the user's input of the project update to the enterprise system by assigning points to the user based on the project update provided to the enterprise system. The assigned points are displayed to the user on a user interface of a user device to enable friendly competition with other users which encourages more detailed and frequent project updates to the enterprise system by the user.

Abstract: Techniques are disclosed for accurately identifying distinct physical user devices in a cross-device context. An example embodiment applies a multi-phase approach to generate labeled training datasets from a corpus of unlabeled device records. Such labeled training datasets can be used for training machine learning systems to predict the occurrence of device records that have been wrongly (or correctly, as the case may be) attributed to different physical user devices. Such identification of improper attribution can be particularly helpful in web-based analytics. The labeled training datasets include labeled pairs of device records generated using multiple strategies for inferring whether the two device records of a pair of device records represent the same physical user device (or different physical user devices). The labeled pairs of device records can then be used to train classifiers to predict with confidence whether two device records represent or do not represent the same physical user device.

Abstract: Described herein is a machine learning framework for facilitating engagements. In accordance with one aspect of the framework, a machine learning model is trained based on the training data. A recommendation associated with an opportunity record may then be generated using the trained machine learning model. Results of one or more actions performed in response to the recommendation may be collected and fed back to the machine learning model to be used as the training data.

Abstract: A framework for improving data set in an enterprise system for machine learning is provided. In accordance with one aspect, user input of a project update is provided by a user to an enterprise system. A record of the project update is created in the enterprise system based on the user input. The project update provided by the user into the enterprise system is analyzed using a gamification technique. The analysis includes quantifying the user's input of the project update to the enterprise system by assigning points to the user based on the project update provided to the enterprise system. The assigned points are displayed to the user on a user interface of a user device to enable friendly competition with other users which encourages more detailed and frequent project updates to the enterprise system by the user.

Abstract: Described herein is a framework for classifying social media inputs. In accordance with one aspect of the framework, one or more social media inputs is acquired from one or more social media platforms. The social media inputs are cleaned to remove redundant elements. One or more features are extracted from the cleaned social media inputs. The social media inputs are classified by a trained classifier into predefined categories using the extracted one or more features.