Crowd sourcing and machine learning based size mapper
AssigneeeBay Inc.
InventorsGaurav Kukal, Dane Glasgow
Life of this patent
Estimating the term…
Abstract
Embodiments for obtaining size and brand information for a plurality of descriptors that include item types and that are associated with user profiles. The descriptors, size, and brand information are obtained by crowdsourcing and by data mining transaction data. Low confidence machine learned data may be boosted by crowdsourcing through targeted questions. Co-occurrences among descriptors are determined and categorized. Signal strength and confidence scores are calculated for the co-occurrences. Relationships between sizes and brands for the item types are calculated and confidence factors for the relationships are calculated.
Claims20
A method comprising:
- obtaining from crowdsourcing and data mining, by at least one computer processor, size and brand information for a plurality of descriptors, the descriptors including item types and associated with user profiles;
- determining and categorizing co-occurrences among descriptors;
- calculating signal strength and confidence scores for the co-occurrences;
- and
- calculating relationships between sizes and brands for the item types.
A machine-readable storage device having embedded therein a set of instructions which, when executed by a machine, causes execution of the following operations:
- obtaining from crowdsourcing and data mining, by at least one computer processor, size and brand information for a plurality of descriptors, the descriptors including item types and associated with user profiles;
- determining and categorizing co-occurrences among descriptors;
- calculating signal strength and confidence scores for the co-occurrences;
- and
- calculating relationships between sizes and brands for the item types.
A system comprising:
- one or more computer processors configured to
- obtain, from crowdsourcing and data mining, size and brand information for a plurality of descriptors, the descriptors including item types and associated with user profiles;
- determine and categorizing co-occurrences among descriptors;
- calculate signal strength and confidence scores for the co-occurrences;
- and
- calculate relationships between sizes and brands for the item types.
Dependencies are inferred from the claim text.