| 摘要 |
<p>A method is provided for performing a content search through comparisons, where a user is presented with two candidate objects and reveals which is closer to the user's intended target object. The disclosed principles provide active strategies for finding the user's target with few comparisons. The so-called rank-net strategy for noiseless user feedback is described. For target distributions with a bounded doubling constant, rank-net finds the target in a number of steps close to the entropy of the target distribution and hence of the optimum. The case of noisy user feedback is also considered. In that context a variant of rank-nets is also described, for which performance bounds within a slowly growing function (doubly logarithmic) of the optimum are found. Numerical evaluations on movie datasets show that rank-net matches the search efficiency of generalized binary search while incurring a smaller computational cost.</p> |
