Abstract: A system and method detect bias and to perform debiasing of an item including data, ideas, and processes, by determining a fairness ratio, from model scores from a recommendation model, sorting the model scores into an ordered list of scores, determining that a sorted score is within a range of predetermined upper and lower thresholds, adjusting the sorted score, recomputing the fairness ratio from the adjusted sorted score, and in the case that the recomputed fairness ratio is not within the range, further adjusting the sorted score until a corresponding fairness ratio of the adjusted sorted score is within the range. In the case that the recomputed fairness ratio is within the range, the system and method generate and output a message with the adjusted sorted score to a recommendation engine, which generates and outputs a recommendation based on the adjusted sorted score.

Abstract: A system and method generate recommendations with cold starts. The system comprises a hardware-based processor, a memory, and a set of modules. The memory stores item taxonomy data for at least one item, stores client descriptive data for at least one client, and stores historical response data for at least one item responded to by the at least one client. The set of modules includes a machine learning module and a recommendation module. The machine learning module generates a response probability matrix using the historical response data, the item taxonomy data, and the client descriptive data. The recommendation module generates and outputs a recommendation corresponding to input data using the information deduced in the response probability matrix. The method implements the system.

Abstract: A system and method generate recommendations with cold starts. The system comprises a hardware-based processor, a memory, and a set of modules. The memory stores item taxonomy data for at least one item, stores client descriptive data for at least one client, and stores historical response data for at least one item responded to by the at least one client. The set of modules includes a machine learning module and a recommendation module. The machine learning module generates a response probability matrix using the historical response data, the item taxonomy data, and the client descriptive data. The recommendation module generates and outputs a recommendation corresponding to input data using the information deduced in the response probability matrix. The method implements the system.

Abstract: A system and method detect bias and to perform debiasing of an item including data, ideas, and processes, by determining a fairness ratio, from model scores from a recommendation model, sorting the model scores into an ordered list of scores, determining that a sorted score is within a range of predetermined upper and lower thresholds, adjusting the sorted score, recomputing the fairness ratio from the adjusted sorted score, and in the case that the recomputed fairness ratio is not within the range, further adjusting the sorted score until a corresponding fairness ratio of the adjusted sorted score is within the range. In the case that the recomputed fairness ratio is within the range, the system and method generate and output a message with the adjusted sorted score to a recommendation engine, which generates and outputs a recommendation based on the adjusted sorted score.

Abstract: At least one processor is configured for defining a plurality of mutually exclusive customer treatment groups, including in accordance with first and second algorithms, to receive content items. Content items are respectively provided to a random customer treatment group as well as first and second algorithm customer treatment groups, and metrics representing at least engagement by each of the customers are determined and analyzed. A selection of the first or the second algorithm is made. The at least one processor is configured to provide, to at least some of the plurality of customers, content items in accordance with the selected algorithm.

Abstract: A system and method generate recommendations with cold starts. The system comprises a hardware-based processor, a memory, and a set of modules. The memory stores item taxonomy data for at least one item, stores client descriptive data for at least one client, and stores historical response data for at least one item responded to by the at least one client. The set of modules includes a machine learning module and a recommendation module. The machine learning module generates a response probability matrix using the historical response data, the item taxonomy data, and the client descriptive data. The recommendation module generates and outputs a recommendation corresponding to input data using the information deduced in the response probability matrix. The method implements the system.