Abstract: Embodiments described herein include moderating listings on an e-commerce site automatically and reporting to moderators. False positive reporting may be rejected using algorithms and filtering of reporting may be handled. Methods for moderating terms of service (ToS) violations may include receiving indications of a ToS violation, generating values corresponding to the ToS violation based on a machine learning (ML) model and the indications of the ToS violation. The vales may be evaluated to determine an actual ToS violation or false positives. The ML model may be updated based on the values and the indications of a ToS violation.

Abstract: Disclosed herein are system, computer-readable storage medium, and method embodiments of automatic ontology generation by embedding representations. A system including at least one processor may be configured to receive a vectorized feature set derived from an embedding and including first and second features, and provide the vectorized feature set to a fuser set including first and second fusers. The system may be configured to generate a representation from the fuser set based on the first and second features, and derive tasks based on the representation, assigning to the tasks respective qualifier sets including a weight value, a loss function, and a feedforward function. The system may be configured to compute respective weighted losses for the tasks, based on the respective qualifier sets, and output a data model based on backpropagating the respective weighted losses through the fuser set, the vectorized feature set, the embedding, or a combination thereof.