  In this paper, we propose an active learning algorithm and models which can
gradually learn individual's preference through pairwise comparisons. The
active learning scheme aims at finding individual's most preferred choice with
minimized number of pairwise comparisons. The pairwise comparisons are encoded
into probabilistic models based on assumptions of choice models and deep
Gaussian processes. The next-to-compare decision is determined by a novel
acquisition function. We benchmark the proposed algorithm and models using
functions with multiple local optima and one public airline itinerary dataset.
The experiments indicate the effectiveness of our active learning algorithm and
models.
