Abstract: A product description generating system dynamically generates a description of an item based on one or more images. The product description generating system includes an attribute machine learning system that receives images of the item and determines at least one attribute value and corresponding prediction confidence value based on the image data. The user review interface provides an interface for reviewing attribute values generated by the attribute machine learning system. If the prediction confidence value is outside a threshold, then the attribute value is reviewed by a user via the user review interface. If, on the other hand, the prediction confidence value is within a threshold, then the attribute value is used to generate the description without user review. The user review interface groups items to be reviewed by taxon and facilitates user review of many items at scale.

Abstract: Methods, apparatus, and processor-readable storage media for detection of anomalous behavior on online platforms using machine learning techniques are provided herein. An example method includes obtaining a set of machine learning models configured to detect anomalous behavior associated with users interacting with an online platform and performing an incremental machine learning process on one or more of the machine learning models in the set. The incremental machine learning process may include obtaining data related to interactions of users with the online platform, updating at least one of the machine learning models in the set based on the obtained data, comparing the machine learning models, and selecting one of the machine learning models from the set to be used by the online platform based on the comparison. The method may further include determining, utilizing the selected machine learning model, that a given user is exhibiting anomalous behavior on the online platform.

Abstract: An apparatus comprises a processing device configured to monitor for events associated with users interacting with an e-commerce platform, to identify an event type of a given event associated with a given user interacting with the e-commerce platform, and to select, based on the identified event type, at least one of a plurality of machine learning models configured to characterize different types of potentially malicious behavior on the e-commerce platform. The processing device is also configured to determine, utilizing the selected at least one machine learning model, whether the given user is exhibiting at least one of the different types of potentially malicious behavior. The processing device is also configured, responsive to determining that the given user is exhibiting at least one of the different types of potentially malicious behavior, to initiate actions on the e-commerce platform to prevent or mitigate an effect of the potentially malicious behavior.

Abstract: An apparatus comprises a processing device configured to monitor for events associated with users interacting with an e-commerce platform, to identify an event type of a given event associated with a given user interacting with the e-commerce platform, and to select, based on the identified event type, at least one of a plurality of machine learning models configured to characterize different types of potentially malicious behavior on the e-commerce platform. The processing device is also configured to determine, utilizing the selected at least one machine learning model, whether the given user is exhibiting at least one of the different types of potentially malicious behavior. The processing device is also configured, responsive to determining that the given user is exhibiting at least one of the different types of potentially malicious behavior, to initiate actions on the e-commerce platform to prevent or mitigate an effect of the potentially malicious behavior.

Abstract: A method, comprising: obtaining an activity signature for a first user of a retail website, the activity signature being based on one or more of navigation data for the first user of the retail website, purchase data for the first user of the retail website, and credit card usage data for the first user of the retail website; detecting whether the first user of the retail website is a reseller by classifying the activity signature for the first user with a neural network; and updating a configuration of a hosting system of the retail website when the first user of the retail website is detected to be a reseller.