A generation device generating an evaluation function for calculating an evaluation value of an evaluation target, the generation device including an acquisition unit acquiring learning data including a qualitative evaluation of the evaluation target; a generation unit generating a constraint to be satisfied by a value of the evaluation function for the evaluation target, based on the learning data; and a setting unit setting weight for a plurality of attributes in the evaluation function so that the constraint is satisfied, and the like are provided.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A device for generating an evaluation function for calculating an evaluation value of an evaluation target, the device comprising: an acquisition unit acquiring learning data including a qualitative evaluation of the evaluation target; a generation unit generating a constraint to be satisfied by a value of the evaluation function for the evaluation target, based on the learning data; and a setting unit setting weight for a plurality of attributes in the evaluation function so that the constraint is satisfied. 2. The device according to claim 1, wherein the acquisition unit acquires the learning data, which includes, as the qualitative evaluation, a comparison result obtained by qualitatively comparing two or more evaluation targets. 3. The device according to claim 1, wherein the acquisition unit acquires the learning data, which includes, as the qualitative evaluation, a comparison result obtained by qualitatively comparing the evaluation target with a predetermined evaluation criterion. 4. The device according to claim 1, wherein the generation unit generates the constraints based on the evaluation function, which includes a term based on a weighted sum of a plurality of basis functions to which an attribute value is input for each attribute of the evaluation target, and wherein the setting unit sets the weight of each of the basis functions so that the constraints are satisfied. 5. The device according to claim 4, wherein the generation unit generates the constraint, which includes a variable indicating whether or not each of the plurality of basis functions is included, and wherein the setting unit optimizes the weights by using an objective function including a total a number of basis functions included in the evaluation function. 6. The device according to claim 5, wherein the generation unit generates the objective function, which includes error variables, and wherein the setting unit optimizes the weights by using the objective function including the error variables. 7. The device according to claim 2, wherein the generation unit generates the constraints, an inequality including a difference in evaluation value of the evaluation function between the two or more evaluation targets as comparison targets and an evaluation threshold as a criterion of the qualitative evaluation, and wherein the setting unit sets a value of the evaluation threshold so that the constraints are satisfied. 8. The device according to claim 7, wherein the acquisition unit acquires the learning data, which includes the qualitative evaluations made by a plurality of evaluators, and wherein the generation unit generates, as the constraints, an inequality including the evaluation threshold for each evaluator. 9. The device according to claim 8, further comprising: a determination unit determining whether or not the difference in evaluation value between the two or more evaluation targets according to the evaluation function based on the weight set by the setting unit falls within a predetermined reference range with respect to the evaluation threshold, wherein in accordance with a determination result that the difference in the evaluation value does not fall within the reference range with respect to the evaluation threshold, the acquisition unit acquires an additional qualitative evaluation and adds the acquired additional qualitative evaluation to the learning data. 10. The device according to claim 9, further comprising: a presentation unit presenting to the evaluator the two or more evaluation targets for which a difference in evaluation value between the two or more evaluation targets falls within a reference range, wherein the acquisition unit acquires an qualitative evaluation made by an evaluator for the presented two or more evaluation targets and adds the acquired qualitative evaluation to the learning data. 11. A computer-implemented method for generating an evaluation function for calculating an evaluation value of an evaluation target, the method comprising: acquiring learning data including a qualitative evaluation of the evaluation target; generating a constraint to be satisfied by a value of the evaluation function for the evaluation target, based on the learning data; and setting weight for a plurality of attributes in the evaluation function so that the constraint is satisfied. 12. The computer-implemented method according to claim 11, wherein the acquiring the learning data, which includes, as the qualitative evaluation, a comparison result obtained by qualitatively comparing two or more evaluation targets. 13. The computer-implemented method according to claim 11, wherein the acquiring the learning data, which includes, as the qualitative evaluation, a comparison result obtained by qualitatively comparing the evaluation target with a predetermined evaluation criterion. 14. The computer-implemented method according to claim 11, wherein the generating the constraints based on the evaluation function, which includes a term based on a weighted sum of a plurality of basis functions to which an attribute value is input for each attribute of the evaluation target, and wherein the setting unit sets the weight of each of the basis functions so that the constraints are satisfied. 15. The computer-implemented method according to claim 14, wherein the generating the constraint, which includes a variable indicating whether or not each of the plurality of basis functions is included, and wherein the setting unit optimizes the weights by using an objective function including a total a number of basis functions included in the evaluation function. 16. The computer-implemented method according to claim 15, wherein the generating the objective function, which includes error variables, and wherein the setting unit optimizes the weights by using the objective function including the error variables. 17. The computer-implemented method according to claim 12, wherein the generating the constraints, an inequality including a difference in evaluation value of the evaluation function between the two or more evaluation targets as comparison targets and an evaluation threshold as a criterion of the qualitative evaluation, and wherein the setting unit sets a value of the evaluation threshold so that the constraints are satisfied. 18. The computer-implemented method according to claim 17, wherein the generating the constraint, which includes a variable indicating whether or not each of a plurality of basis functions is included, and wherein the setting unit optimizes the weights by using an objective function including a total a number of basis functions included in the evaluation function. 19. A non-transitory computer program product for generating an evaluation function for calculating an evaluation value of an evaluation target comprising a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code configured to perform: acquiring learning data including a qualitative evaluation of the evaluation target; generating a constraint to be satisfied by a value of the evaluation function for the evaluation target, based on the learning data; and setting weight for a plurality of attributes in the evaluation function so that the constraint is satisfied. 20. The non-transitory computer program product according to claim 19, wherein the acquiring the learning data, which includes, as the qualitative evaluation, a comparison result obtained by qualitatively comparing two or more evaluation targets.