| 摘要 |
<p>In interactive content search through comparisons, a search for a target object in a database is performed by finding the object most similar to the target from a small list of objects. A new object list is then presented based on the earlier selections. This process is repeated until the target is included in the list presented, at which point the search terminates. A solution to the interactive content search problem is provided under the scenario of heterogeneous demand, where target objects are selected from a non-uniform probability distribution. It has been assumed that objects are embedded in a doubling metric space which is fully observable to the search algorithm. Based on these assumptions, an efficient comparison-based search method is provided whose cost in terms of the number of queries can be bounded by the doubling constant of the embedding c, and the entropy of demand distribution, H. More precisely, the present principles show that the average search costs scales CF=O(c5H), which improves upon the previously best known bound and is order optimal for constant c.</p> |
