  Machine Learning's proliferation in critical fields such as healthcare,
banking, and criminal justice has motivated the creation of tools which ensure
trust and transparency in ML models. One such tool is Actionable Recourse (AR)
for negatively impacted users. AR describes recommendations of cost-efficient
changes to a user's actionable features to help them obtain favorable outcomes.
Existing approaches for providing recourse optimize for properties such as
proximity, sparsity, validity, and distance-based costs. However, an
often-overlooked but crucial requirement for actionability is a consideration
of User Preference to guide the recourse generation process. In this work, we
attempt to capture user preferences via soft constraints in three simple forms:
i) scoring continuous features, ii) bounding feature values and iii) ranking
categorical features. Finally, we propose a gradient-based approach to identify
User Preferred Actionable Recourse (UP-AR). We carried out extensive
experiments to verify the effectiveness of our approach.
