Abstract: Techniques are described herein for performing generation of vector-based recommendations for entities. One or more embodiments address the challenge of generating recommendations for entities lacking sufficient recommendation generation input data. Unlike traditional collaborative filtering methods, various embodiments leverage content filtering techniques to make tailored and relevant recommendations for entities. By analyzing entity attributes and generating entity attribute vectors in an N-dimensional space, this system identifies the most suitable transactions by determining the closest neighbors in this space, thereby enhancing the accuracy and effectiveness of recommendations for entities associated with insufficient recommendation generation input data within the platform.

Abstract: Techniques for generating terms to replace an initial set of search terms for a query are disclosed. A system generates a training data set for training a machine learning model. Generating the training data set includes generating search value vectors for each of a set of labels based on sets of search values associated respectively with the labels in the set of labels. The system trains a machine learning model to predict a target label for a target search vector based on the set of labels and the respectively associated search value vectors. The system generates a target search value vector based on an initial set of search values. The system then applies the trained machine learning model to the target search value vector to predict the target label. The target label is used as a search term, that replaces the initial set of search values, for executing the query.

Abstract: Techniques for providing cross-cluster transaction risk assessment are disclosed herein.

Abstract: Techniques for generating terms to replace an initial set of search terms for a query are disclosed. A system generates a training data set for training a machine learning model. Generating the training data set includes generating search value vectors for each of a set of labels based on sets of search values associated respectively with the labels in the set of labels. The system trains a machine learning model to predict a target label for a target search vector based on the set of labels and the respectively associated search value vectors. The system generates a target search value vector based on an initial set of search values. The system then applies the trained machine learning model to the target search value vector to predict the target label. The target label is used as a search term, that replaces the initial set of search values, for executing the query.