Robust Best Point Selection under Unreliable User Feedback

The task of finding a user's utility function (representing the user's preference) by asking them to compare pairs of points through a series of questions, each requiring him/her to compare 2 points for choosing a more preferred one, to find the best point in the database is a common problem in the database community. However, in real-world scenarios, users may provide unreliable answers due to two major types of errors, namely persistent errors and random errors. Existing interaction algorithms either simply assume that all answers provided by the user are reliable, or are capable of handling random errors only, which can lead to finding undesirable points, ignoring persistent errors. To address this challenge, we propose more generalized algorithms that are robust to both persistent and random errors made by the user. Specifically, we propose (1) an algorithm that asks an asymptotically optimal number of questions, and (2) an algorithm that asks an even smaller number of questions empirically, with provable performance guarantee. Our experiments on both real and synthetic datasets demonstrate that our algorithms outperform existing methods in terms of accuracy, even with a small number of questions asked.