Abstract: This application relates to apparatus and methods for automatically grouping the same or similar items provided by various suppliers that may use various supplier identification systems to identify the items to a retailer. In some examples, item update data identifying an attribute of an item is received and an anomaly is detected in a hierarchical structure of a database. The anomaly is detected by a machine learning model trained by a supervised training dataset based on attributes of items. A hierarchical association between the attribute of the item and the item in the database is generated when the detected anomaly includes a difference between a first hierarchical relationship defined in the item update data and a second hierarchical relationship defined for the item in the database. Item data including the hierarchical association is transmitted to update at least one marketplace.

Abstract: This application relates to apparatus and methods for automatically grouping the same or similar items provided by various suppliers that may use various supplier identification systems to identify the items to a retailer. In some examples, a computing device receives a supplier identifier from a supplier. The supplier identifier identifies an item that the supplier provides to the retailer for sale. The computing device may determine a group identifier for the item, where the group identifier identifies the item within a group of the retailer. The computing device may update the supplier identifier's association to the retailer identifier based on whether the group identifier is also associated with the retailer identifier. In some examples, the computing device executes a machine learning model to determine anomalies within a current database of supplier identifiers and associated retailer identifiers. In some examples, the computing device indicates changes to associations and any detected anomalies.

Abstract: This application relates to apparatus and methods for automatically grouping the same or similar items provided by various suppliers that may use various supplier identification systems to identify the items to a retailer. In some examples, a computing device receives a supplier identifier from a supplier. The supplier identifier identifies an item that the supplier provides to the retailer for sale. The computing device may determine a group identifier for the item, where the group identifier identifies the item within a group of the retailer. The computing device may update the supplier identifier's association to the retailer identifier based on whether the group identifier is also associated with the retailer identifier. In some examples, the computing device executes a machine learning model to determine anomalies within a current database of supplier identifiers and associated retailer identifiers. In some examples, the computing device indicates changes to associations and any detected anomalies.