Generating actionable algorithmic recourse requires understanding each user’s preferences. Users provide their relevant information, and the system uses it to generate recourse that can be easily followed by individual users. To gain insight into users’ perceptions and experiences of this novel form of interaction, we developed a prototype that enables users to provide the required information for algorithmic recourse customization. With the prototype, we conducted a user study where participants customized the recourse. Through both quantitative and qualitative analysis, we found that: (1) repetitive user-AI interaction not only enables users to customize the recourse but also explore other possibilities, (2) users prefer recourse customization method that offers high controllability and understandability, and (3) degree of customization users want depends on various factors. With these findings, we discuss the implications for systems that aim to provide actionable algorithmic recourse in real-life situations.