CROWD SOURCING AND MACHINE LEARNING BASED SIZE MAPPER
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.
1 . 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.
2 . The method of claim 1 further comprising boosting confidence for machine learned data with low confidence.
3 . The method of claim 2 wherein boosting confidence for machine learned data with low confidence comprises asking targeted questions to users.
4 . The method of claim 2 wherein low confidence data from machine learning is picked based on at least one of the quantities consisting of a frequency score for a particular item type for a profile, the number of days that have passed since the capture of a transaction in a profile record, and the variation in size for the same item type in a profile.
5 . The method of claim 1 wherein calculating signal strength uses a constant number for dampening the effect of signals in a co-occurrence that come from machine learning data.
6 . The method of claim 1 wherein the records of a co-occurrence include time stamps and categorizing descriptors comprises placing co-occurrences into logical categories based on the time-gap between time stamps of two records of the co-occurrence.
7 . The method of claim 1 wherein the confidence of the relationships may be calculated based on the signal score of profile of co-occurrences, the time-gap between records of co-occurrences, and frequency scores of profiles used in calculating the relationships.
8 . 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.
9 . The machine-readable storage device of claim 8 further comprising boosting confidence for co-occurrences with low confidence.
10 . The machine-readable storage device of claim 9 wherein boosting confidence for co-occurrences with low confidence comprises asking targeted questions to users.
11 . The machine-readable storage device of claim 9 wherein low confidence data from machine learning is picked based on at least one of the quantities consisting of a frequency score for a particular item type for a profile, the number of days that have passed since the capture of a transaction in a profile record, and the variation in size for the same item type in a profile.
12 . The machine-readable storage device of claim 8 wherein calculating signal strength uses a constant number for dampening the effect of signals in a co-occurrence that come from machine learning data.
13 . The machine-readable storage device of claim 8 wherein the records of a co-occurrence include time stamps and categorizing descriptors comprises placing co-occurrences into logical categories based on the time-gap between time stamps of two records of the co-occurrence.
14 . The machine-readable storage device of claim 8 wherein the confidence of the relationships may be calculated based on the signal score of profiles in co-occurrences, the time-gap between records of co-occurrences, and frequency scores of profiles used in calculating the relationships.
15 . 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.
16 . The system of claim 15 the one or more computer processors further configured to boost confidence for co-occurrences with low confidence.
17 . The system of claim 15 wherein low confidence data from machine learning is picked based on at least one of the quantities consisting of a frequency score for a particular item type for a profile, the number of days that have passed since the capture of a transaction in a profile record, and the variation in size for the same item type in a profile.
18 . The system of claim 15 wherein calculating signal strength uses a constant number for dampening the effect of signals in a co-occurrence that come from machine learning data.
19 . The system of claim 15 wherein the records of a co-occurrence include time stamps and categorizing descriptors comprises placing co-occurrences into logical categories based on the time-gap between time stamps of two records of the co-occurrence.
20 . The system of claim 15 wherein the confidence of the relationships may be calculated based on the signal score of profiles in co-occurrences, the time-gap between records of co-occurrences, and frequency scores of profiles used in calculating the relationships.