A method and apparatus for generating a training model based on feedback are provided. The method for generating a training model based on feedback, includes calculating an eigenvector of a sample among a plurality of samples; obtaining scores granted by a user for one or more of the plurality of samples in a round, obtaining scores granted by the user for a first number of samples; obtaining scores granted by the user for a second number of samples in response to detecting, based on the eigenvector, an inconsistency between the scores granted by the user for the first number of samples; and generating a training model based on the scores granted by the user for the first and second numbers of samples. A corresponding apparatus is also provided.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus for generating a training model based on feedback, comprising: a calculating module configured to calculate an eigenvector of a sample among a plurality of samples; an obtaining module configured to: obtain scores granted by at least one user for at least one of the plurality of samples in at least one round, obtain scores granted by the at least one user for a first number of samples, and obtain scores granted by the at least one user for a second number of samples in response to having detected, based on the eigenvector, an inconsistency in the scores granted by the at least one user for the first number of samples; and a generating module configured to generate a training model, stored in non-transitory computer readable storage media, based on the scores granted by the at least one user for the first and second numbers of samples, the training model to rate and/or condition data for other samples. 2. The apparatus according to claim 1, wherein the obtaining module further comprises: a first obtaining module configured to obtain scores granted by the at least one user for at least one of the plurality of samples in at least one round; and a second obtaining module configured to obtain scores granted by the at least one user for the second number of samples in response to having detected, based on the eigenvector, an inconsistency in the scores granted by the at least one user for the first number of samples. 3. The apparatus according to claim 2, further comprising: a clustering module configured to cluster the plurality of samples into at least one group based on the eigenvector. 4. The apparatus according to claim 2, wherein the first and second obtaining modules comprise: a third obtaining module configured to obtain a score granted by the at least one user for at least one sample in each group of the at least one group. 5. The apparatus according to claim 2, wherein the first and second obtaining modules further comprise: a providing module configured to, for one user among the at least one user, provide at least two samples; a fifth obtaining module configured to obtain original scores granted by the user for the at least two samples; and a normalizing module configured to normalize the original scores to obtain scores granted by the user for the at least two samples. 6. The apparatus according to claim 2, wherein the second obtaining module comprises: a sixth obtaining module configured to, if a difference between scores granted by a first user among the at least one user for similar samples exceeds a first threshold, provide more similar samples to the first user to obtain scores granted by the first user for the more similar samples. 7. The apparatus according to claim 2, wherein the second obtaining module further comprises: a seventh obtaining module configured to, if a difference between scores granted by similar users for a first sample of the plurality of samples exceeds a second threshold, provide the first sample once again to the similar users respectively to obtain scores granted by the similar users for the first sample. 8. The apparatus according to claim 2, wherein the second obtaining module further comprises: an eighth obtaining module configured to, if a gap between a difference between original scores granted by one user among the at least one user for two samples and a difference between normalized scores exceeds a third threshold, substitute one of the two samples with another sample of the plurality of samples. 9. The apparatus according to claim 1, wherein the generating module comprises: a filling module configured to fill in a scoring matrix the scores granted by the at least one user for the first and second numbers of samples; and a second generating module configured to collaboratively filter the scoring matrix to generate the training model. 10. The apparatus according to claim 1, wherein the samples include image data.