Seller risk detection by product community and supply chain modelling with only transaction records

A system for configuring a search engine to classify a search query, the system comprising:

• a processor;
• and
• a non-transitory computer-readable medium having stored thereon instructions that are executable by the processor to cause the system to perform operations comprising:
• defining a first data vector of a first entity based on a set of data records associated with representative activities of first users associated with the first entity,
• wherein the set of data records comprises:
• product data associated with the first entity, and
• at least one relationship data representing an activities relationship between the first entity and a plurality of second entities,
• wherein the first data vector encodes the product data and the at least one relationship data of the first entity;
• defining a second data vector of the first entity based on a data representation of a plurality of topological connections between the plurality of second entities and the first entity,
• wherein the plurality of topological connections are indicative of a shape of a plurality of relationships between the first entity and at least one of the plurality of second entities within a supply chain of the first entity;
• utilizing a clustering space machine learning model to generate a multi-dimensional clustering space comprising an entity vector representing the first entity based on the first data vector and the second data vector;
• and
• generating an entity-specific classification of the first entity based on the multi-dimensional clustering space.

A method comprising:

• defining, by a processor, a first data vector of a first entity based on a set of data records associated with representative activities of the first entity,
• wherein the set of data records comprise:
• product data associated with the first entity, and
• at least one relationship data representing an activities relationship between the first entity and a plurality of second entities,
• wherein the first data vector encodes the product data and the at least one relationship data of the first entity;
• defining, by the processor, a second data vector of the first entity based on a data representation of a plurality of topological connections between a plurality of second entities and the first entity,
• wherein the plurality of topological connections represents a shape indicative of a plurality of relationships between the first entity and at least one of the plurality of second entities within a supply chain of the first entity;
• generating a prediction that the first entity exhibits a characteristic by inputting the first data vector and the second data vector into a multi-dimensional clustering space machine learning model;
• wherein the prediction is generated based at least in part on a determination that the shape represented by the plurality of topological connections is similar to a reference shape represented by a plurality of topological connections of one of the plurality of second entities that has been identified as having the characteristic.