## Interactive Regret Minimization

Author: Danupon Nanongkai, Atish Das Sarma, Ashwin Lall, Kazuhisa Makino
(Author names are NOT in alphabetical order. )

We study the notion of regret ratio proposed by Nanongkai et al. [VLDB’10] to deal with multi-criteria decision making in database systems. The regret minimization query proposed Nanongkai et al. was shown to have features of both skyline and top-$k$: it does not need information from the user but still controls the output size. While this approach is suitable for obtaining a reasonably small regret ratio, it is still open whether one can make the regret ratio arbitrarily small. Moreover, it remains open whether reasonable questions can be asked to the users in order to improve efficiency of the process.