The present invention relates to a method and an apparatus for estimating multiple-ranking using pairwise comparative data, the method according to one embodiment comprising the steps of: inputting information for pairwise comparative data as input; and generating multiple-ranking data on the basis of the input data, wherein the input data comprises a collection of a plurality of respondents, a collection of a plurality of items, and a collection of a plurality of pairwise comparative data for the plurality of items, and each item of pairwise comparative data in the plurality of same is data indicating the preference between two items determined in accordance with an evaluation criterion from among a plurality of evaluation criteria.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for estimating multi-ranking using pairwise comparison data, the method comprising: inputting information on pairwise comparison data as input data; and generating multi-ranking data based on the input data, wherein the input data comprises a collection of a plurality of respondents, a collection of a plurality of items, and a collection of a plurality of pairwise comparison data regarding the plurality of items, and wherein each of the plurality of pairwise comparison data is data indicating preference between two items determined according to one of a plurality of evaluation criteria. 2. The method of claim 1, wherein the multi-ranking data comprises: an evaluation criterion preference probability distribution (θu,m) which is a probability distribution of a probability that a respondent selects a certain evaluation criterion; an item score (πi(m)) which is a score given to each item according to each evaluation criterion; and a response accuracy probability distribution (ηm) which is a probability distribution of a probability that a respondent accurately responds according to each evaluation criterion. 3. The method of claim 2, wherein the generating the multi-ranking data comprises: calculating a probability (Pr) of a pairwise comparison result regarding two certain items (oi, oj) based on the input data and first multi-ranking data; calculating a likelihood function based on the calculated probability (Pr); and calculating second multi-ranking data for maximizing the likelihood function. 4. The method of claim 3, wherein the first multi-ranking data comprises a prior evaluation criterion preference probability distribution, a prior item score, and a prior response accuracy probability distribution, and wherein the second multi-ranking data comprises a posterior evaluation criterion preference probability distribution, a posterior item score, and a posterior response accuracy probability distribution. 5. The method of claim 4, wherein the probability (Pr) of the pairwise comparison result is a probability of a result of comparison indicating that a first item (oi) is preferred to a second item (oj), and wherein, regarding each evaluation criterion, the probability (Pr) of the pairwise comparison result is in proportion to a sum of: a probability of a comparison result indicating that the first item (oi) is preferred to the second item (oj) when the respondent correctly responds; and a probability of a comparison result indicating that the second item (oj) is preferred to the first item (oi) when the respondent incorrectly responds. 6. The method of claim 5, wherein the probability of the result of the comparison indicating that that the first item (oi) is preferred to the second item (oj) and the probability of the result of the comparison indicating that the second item (oj) is preferred to the first item (oi) follow a Bradley-Terry model. 7. The method of claim 4, wherein the prior evaluation criterion preference probability distribution follows a Dirichlet distribution, and the prior response accuracy probability distribution follows a beta distribution. 8. The method of claim 4, wherein the calculating the second multi-ranking data for maximizing the likelihood function comprises calculating the second multi-ranking data by using an Expectation Maximization (EM) algorithm. 9. The method of claim 4, wherein the calculating the second multi-ranking data for maximizing the likelihood function comprises calculating the second multi-ranking data by using an Expectation Maximization (EM) algorithm and an Least Square Method (LSM) algorithm. 10. The method of claim 9, wherein the calculating the second multi-ranking data for maximizing the likelihood function comprises: calculating the posterior evaluation criterion preference probability distribution, the posterior response accuracy probability distribution, and a score difference between posterior items scores of the first item (oi) and the second item (oj) according to the EM algorithm; and calculating a posterior score of each item according to the LSM algorithm. 11. A computer readable recording medium which has a program recorded thereon, for executing the method according to claim 1 in a computer.