Abstract: A topic mapping system generates customized mapping schemas for multiple topic sets. The topic mapping system generates document clusters that represent groups of digital documents. The topic mapping system also generates, for each topic set, a document-topic mapping data object (“DTM data object”) that describes a customized mapping schema of the document clusters to labels in the topic set. The topic mapping system identifies customized groups of documents for responding to multiple requests that have a particular keyword. For each request, the topic mapping system identifies a particular topic set and DTM data object associated with a computing system that provided the request. Based on the keyword, the topic mapping system identifies documents that are categorized according to the customized mapping schema in the DTM data object. The topic mapping system can provide customized groups of documents to respective computing systems that provided the multiple requests.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently predicting conversion probability scores and key personas for target entities utilizing an artificial intelligence approach. For example, the disclosed systems utilize a conversion activity score neural network to predict conversion activity probability scores for target entities and utilize a persona prediction machine learning model to predict key personas associated with target entities. In particular, the disclosed systems utilize the conversion activity score neural network to generate a predicted conversion activity probability score for a target entity from input data including client device interactions of digital profiles belonging to the target entity as well as an entity feature vector representing characteristics of the target entity.

Abstract: The present disclosure generally relates to techniques for predicting a collective decision made by a group of users on behalf of a requesting entity. A predictive analysis system includes specialized machine-learning architecture that generates a prediction of a collective group decision based on the captured interactions of individual members of the group.

Abstract: An improved electronic communication system schedules transmission of electronic communications based on a predicted open time and click time. The open and click times are predicted from a machine learning model that is trained to optimize for both tasks. Additionally, when training the machine learning model, the loss used for adjusting the system to achieve a desired accuracy may be a biased loss determined from a function that penalizes overpredicting the open time. As such, the loss value may be determined by different set of rules depending on whether the predicted time is greater than the actual time or not.

Abstract: Techniques are disclosed for selecting audience members for a marketing campaign. A list of potential members is accessed, where each member is associated with a corresponding feature vector comprising features. A subset of the features is selected, and used to select a first group from the list for inclusion in the campaign, thereby also defining a second group from the list for exclusion from the campaign. A first similarity among the members in the first group is compared to a second similarity between the members in the first and second groups. If the first similarity is equal to or lower than the second similarity, the subset of features is updated to form a new subset of features, and the selection process of target audience member is repeated, until the first similarity becomes higher than the second similarity. Subsequently, the marketing campaign is launched with the first group of members.