Predictive recommendation system using price boosting
In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers. In embodiments, predictive models based on gross revenue may be optimized using promotion category-dependent price boosting.
1. A system, comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to:
determine a relevance score for each promotion of an available promotions set based at least in part on promotion data and consumer data, the promotion data comprising promotion attributes of the promotion, and the consumer data comprising consumer attributes associated with a consumer identifier;
determine an available promotions subset by selecting those available promotions from the available promotions set having a respective relevance score above a relevance score threshold;
determine an optimal promotion category for the consumer identifier based at least in part on location data associated with the consumer identifier and a total count of available promotions in the available promotions subset;
determine an adjusted available promotions subset based on at least in part on filtering the available promotions subset according to the optimal promotion category; and
transmit the adjusted available promotions subset to a computing device associated with the consumer identifier.
2. The system of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more computers, to further cause the one or more computers to:
determine the relevance score for each promotion based on promotion price data comprising promotion price attributes for the promotion.
3. The system of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more computers, to further cause the one or more computers to:
determine the relevance score for each promotion based on a ranking target variable associated with a predicted consumer interaction with respect to the promotion.
4. The system of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more computers, to further cause the one or more computers to:
select promotion feature data elements of a feature vector data structure associated with the promotion data based on a ranking target variable associated with a predicted consumer interaction with respect to each promotion.
5. The system of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more computers, to further cause the one or more computers to:
determine the relevance score for each promotion based on consumer behavior data associated with the consumer identifier.
6. The system of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more computers, to further cause the one or more computers to:
select consumer feature data elements of a feature vector data structure associated with the consumer data based on a ranking target variable associated with a predicted consumer interaction with respect to each promotion.
7. The system of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more computers, to further cause the one or more computers to:
select promotions for the available promotions set in response to a search query associated with the computing device.
8. The system of claim 1 , wherein the one or more storage devices store instructions that are operable, when executed by the one or more computers, to further cause the one or more computers to:
determine the adjusted available promotions subset based on the available promotions subset and an optimal promotion discovery region for the available promotions in the available promotions subset.
9. A computer-implemented method, comprising:
determining, by a device comprising a processor, a relevance score for each promotion of an available promotions set based at least in part on promotion data and consumer data, the promotion data comprising promotion attributes of the promotion, and the consumer data comprising consumer attributes associated with a consumer identifier;
determining, by the device, an available promotions subset by selecting those available promotions from the available promotions set having a respective relevance score above a relevance score threshold;
determining, by the device, an optimal promotion category for the consumer identifier based at least in part on location data associated with the consumer identifier and a total count of available promotions in the available promotions subset;
determining, by the device, an adjusted available promotions subset based on at least in part on filtering the available promotions subset according to the optimal promotion category; and
transmitting, by the device, the adjusted available promotions subset to a computing device associated with the consumer identifier.
10. The computer-implemented method of claim 9 , wherein the determining the relevance score comprises determining the relevance score for each promotion based on promotion price data comprising promotion price attributes for the promotion.
11. The computer-implemented method of claim 9 , wherein the determining the relevance score comprises determining the relevance score for each promotion based on a ranking target variable associated with a predicted consumer interaction with respect to the promotion.
12. The computer-implemented method of claim 9 , further comprising:
selecting, by the device, promotion feature data elements of a feature vector data structure associated with the promotion data based on a ranking target variable associated with a predicted consumer interaction with respect to each promotion.
13. The computer-implemented method of claim 9 , wherein the determining the relevance score comprises determining the relevance score for each promotion based on consumer behavior data associated with the consumer identifier.
14. The computer-implemented method of claim 9 , further comprising:
selecting, by the device, consumer feature data elements of a feature vector data structure associated with the consumer data based on a ranking target variable associated with a predicted consumer interaction with respect to each promotion.
15. The computer-implemented method of claim 9 , further comprising:
selecting, by the device, promotions for the available promotions set in response to a search query associated with the computing device.
16. The computer-implemented method of claim 9 , wherein the determining the adjusted available promotions subset comprises determining the adjusted available promotions subset based on the available promotions subset and an optimal promotion discovery region for the available promotions in the available promotions subset.
17. A computer program product, stored on a computer readable medium, comprising instructions that when executed by one or more computers cause the one or more computers to:
determine a relevance score for each promotion of an available promotions set based at least in part on promotion data and consumer data, the promotion data comprising promotion attributes of the promotion, and the consumer data comprising consumer attributes associated with a consumer identifier;
determine an available promotions subset by selecting those available promotions from the available promotions set having a respective relevance score above a relevance score threshold;
determine an optimal promotion category for the consumer identifier based at least in part on location data associated with the consumer identifier and a total count of available promotions in the available promotions subset;
determine an adjusted available promotions subset based on at least in part on filtering the available promotions subset according to the optimal promotion category; and
transmit the adjusted available promotions subset to a computing device associated with the consumer identifier.
18. The computer program product of claim 17 , wherein the computer program product further comprises instructions that when executed by the one or more computers cause the one or more computers to:
determine the relevance score for each promotion based on promotion price data comprising promotion price attributes for the promotion.
19. The computer program product of claim 17 , wherein the computer program product further comprises instructions that when executed by the one or more computers cause the one or more computers to:
determine the relevance score for each promotion based on a ranking target variable associated with a predicted consumer interaction with respect to the promotion.
20. The computer program product of claim 17 , wherein the computer program product further comprises instructions that when executed by the one or more computers cause the one or more computers to:
determine the relevance score for each promotion based on consumer behavior data associated with the consumer identifier.