According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.
Please help me write a proper abstract based on the patent claims. CLAIM: 1.-8. (canceled) 9. A system to generate a modified material composition, the system comprising: a memory having computer readable instructions; and a processor configured to execute the computer readable instructions, the computer readable instructions comprising: receiving, by the processor, one or more input compositions, each input composition comprising one or more materials; assigning a material vector to each of the materials; learning for each of the one or more input compositions a composition vector based on the material vectors of the materials that form each composition; assigning a predicted rating value to each of the composition vectors, each predicted rating value having a confidence level; selecting a composition to be rated based on the confidence levels of the composition vectors; presenting the selected composition to be rated to a user; receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user; and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value. 10. The system claim 9, wherein the material vectors are learned by training an artificial neural network. 11. The system of claim 10, wherein inputs and outputs of the artificial neural network include one or more materials from a prior example composition. 12. The system of claim 9, wherein the composition vector is learned based on the material vectors of respective materials of respective compositions and proportions of the materials within the respective compositions. 13. The system of claim 12, wherein ensemble-normalized proportion weights are computed for each material. 14. The system of claim 13, wherein the ensemble-normalized weights are responsive to at least one of a distribution of a material percentage of the material within a group of compositions or a normalization of a material percentage of the one or more materials within a single composition by at least one distribution parameter. 15. The system of claim 9, wherein the materials are ingredients and the compositions are recipes. 16. The system of claim 9, further comprising recommending a new composition to the user based on the predictive model. 17. A computer program product to generate a material composition, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: receive one or more input compositions, each input composition comprising one or more materials; assign a material vector to each of the materials; learn for each of the one or more input compositions a composition vector based on the material vectors of the materials that form each composition; assign a predicted rating value to each of the composition vectors, each predicted rating value having a confidence level; select a composition to be rated based on the confidence levels of the composition vectors; present the selected composition to be rated to a user; receive a user rating for the composition to be rated; adjust the predicted rating values and confidence levels of the composition vectors that have not been rated by the user; and generate a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value. 18. The computer program product of claim 17, wherein the material vectors are learned by training an artificial neural network. 19. The computer program product of claim 18, wherein the materials are ingredients and the compositions are recipes. 20. The computer program product of claim 18, further causing the processor to recommend a new composition to the user based on the predictive model.