Abstract: Methods and systems are presented for tagging an account associated with a user based on a predicted likelihood of an event associated with the user. A set of features is determined for data associated with the user. Values from the data are aggregated over time intervals for each feature to create time series data. The time series data is used as input to a neural network configured to accept input with the determined features. A predictive value indicating the likelihood of an event associated with the user is received from the neural network and used to determine whether to tag a user account. Determinations regarding the user are made based on the existence of absence of a tag on the user's account.

Abstract: Methods and systems are presented for tagging an account associated with a user based on a predicted likelihood of an event associated with the user. A set of features is determined for data associated with the user. Values from the data are aggregated over time intervals for each feature to create time series data. The time series data is used as input to a neural network configured to accept input with the determined features. A predictive value indicating the likelihood of an event associated with the user is received from the neural network and used to determine whether to tag a user account. Determinations regarding the user are made based on the existence of absence of a tag on the user's account.

Abstract: Methods and systems are presented for configuring and training a machine learning model using transfer learning techniques that can transfer knowledge among multiple domains that do not share an identical feature set. Instead of using any feature set associated with a domain, a feature arrangement that combines all of the feature sets associated with the multiple domains in a particular organization for configuring and training the machine learning model. The feature arrangement includes a domain independent section and multiple domain-specific sections corresponding to the multiple domains. The domain independent section includes common features that are common across the multiple domains. Each of the domain-specific sections includes a feature set associated with the corresponding domain. The machine learning model that is configured in this manner can be trained to learn knowledge across the multiple domains and subsequently perform tasks for the multiple domains.

Abstract: Methods and systems are presented for tagging an account associated with a user based on a predicted likelihood of an event associated with the user. A set of features is determined for data associated with the user. Values from the data are aggregated over time intervals for each feature to create time series data. The time series data is used as input to a neural network configured to accept input with the determined features. A predictive value indicating the likelihood of an event associated with the user is received from the neural network and used to determine whether to tag a user account. Determinations regarding the user are made based on the existence of absence of a tag on the user's account.