Abstract: Embodiments of the disclosure relate to systems and methods for leveraging unsupervised machine learning to produce interpretable routing rules. In various embodiments, a training dataset comprising a plurality of data records is created. The plurality of data records includes message data comprising a plurality of messages and action data comprising a plurality of actions that correspond to the plurality of messages. A first machine learning model is trained using the training dataset. The first machine learning model as trained provides cluster data that indicates, for each data record of the plurality of data records of the training dataset, membership in a cluster of a plurality of clusters. An enhanced training dataset is created that comprises the message data from the training dataset, the action data from the training dataset, and the cluster data.

Abstract: Embodiments of the disclosure relate to systems and methods for leveraging unsupervised machine learning to produce interpretable routing rules. In various embodiments, a training dataset comprising a plurality of data records is created. The plurality of data records includes message data comprising a plurality of messages and action data comprising a plurality of actions that correspond to the plurality of messages. A first machine learning model is trained using the training dataset. The first machine learning model as trained provides cluster data that indicates, for each data record of the plurality of data records of the training dataset, membership in a cluster of a plurality of clusters. An enhanced training dataset is created that comprises the message data from the training dataset, the action data from the training dataset, and the cluster data.

Abstract: In general, embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive document recommendation in collaborative document server systems. In one example, a method comprises generating a recommended collaborative document set for a primary collaborative document using a cross-document correlation model that is characterized by a temporally correlated document viewing sequence set and a cross-user correlation model that is characterized by a qualifying user relationship set.

Abstract: A collaboration tool configured to: (1) automatically determine a topic-specific reputation score for one or more users of the collaboration tool, (2) to store the topic-specific reputation scores in a reputation database, (3) to access the reputation database in response to receiving a user input associated with a particular topic from another user, and (4) display to the other user a list of one or more users of the collaboration tool having topic-specific reputation scores exceeding a threshold.

Abstract: A collaboration tool configured to: (1) automatically determine a topic-specific reputation score for one or more users of the collaboration tool, (2) to store the topic-specific reputation scores in a reputation database, (3) to access the reputation database in response to receiving a user input associated with a particular topic from another user, and (4) display to the other user a list of one or more users of the collaboration tool having topic-specific reputation scores exceeding a threshold.

Abstract: Embodiments of the disclosure relate to systems and methods for leveraging unsupervised machine learning to produce interpretable routing rules. In various embodiments, a training dataset comprising a plurality of data records is created. The plurality of data records includes message data comprising a plurality of messages and action data comprising a plurality of actions that correspond to the plurality of messages. A first machine learning model is trained using the training dataset. The first machine learning model as trained provides cluster data that indicates, for each data record of the plurality of data records of the training dataset, membership in a cluster of a plurality of clusters. An enhanced training dataset is created that comprises the message data from the training dataset, the action data from the training dataset, and the cluster data.