Recently, interactive evolutionary computation (IEC) has been extensively applied in those systems that recommend objects, such as images and sounds, to users based on their preference. If an IEC user's evaluation criteria are clearly known, they can be utilized for acceleration of IEC, merchandise development, and creativity support for designers. It is difficult to collect a large volume of evaluation data for their analysis because an IEC user cannot repeat the evaluation so many times. Therefore, the technique proposed in this study adopts paired comparison-based interactive differential evolution (IDE) to ease the burden of users, and it will extract the user evaluation criteria through less number of evaluation steps. These techniques estimate the user evaluation criteria using the distribution of solutions because IDE does not receive the evaluation values from its user. Techniques are proposed that estimate, through the IEC processes, the degree of influence of each variable on the evaluation by any given user. During the simulations, the proposed methods are evaluated on test problems.