A comparison-based search algorithm lets a user find a target item $t$ in a database by answering queries of the form, ``Which of items $i$ and $j$ is closer to $t$?'' Instead of formulating an explicit query (such as one or several keywords), the user navigates towards the target via a sequence of such (typically noisy) queries. We propose a scale-free probabilistic oracle model called $\gamma$-CKL for such similarity triplets $(i,j;t)$, which generalizes the CKL triplet model proposed in the literature. The generalization affords independent control over the discriminating power of the oracle and the dimension of the feature space containing the items. We develop a search algorithm with provably exponential rate of convergence under the $\gamma$-CKL oracle, thanks to a backtracking strategy that deals with the unavoidable errors in updating the belief region around the target. We evaluate the performance of the algorithm both over the posited oracle and over several real-world triplet datasets. We also report on a comprehensive user study, where human subjects navigate a database of face portraits.

翻译：基于比较的搜索算法允许用户通过回答形如“项目$i$和$j$中哪个更接近目标$t$？”的查询来在数据库中寻找目标项$t$。用户无需提出显式查询（如一个或多个关键词），而是通过一系列（通常带有噪声的）此类查询逐步向目标导航。我们针对此类相似性三元组$(i,j;t)$提出一种称为$\gamma$-CKL的无尺度概率先验模型，该模型推广了文献中提出的CKL三元组模型。该泛化能独立控制先验的区分能力与包含项目特征空间的维度。我们开发了一种在$\gamma$-CKL先验下具有指数级收敛速度的搜索算法，该算法通过引入回溯策略处理更新目标周围置信区域时不可避免的误差。我们在所提出的先验模型及多个真实三元组数据集上评估了算法性能，并报告了一项全面的人机实验——实验中人类受试者在人脸肖像数据库中进行导航。