Matching algorithms draw on user-provided information to propose “optimal” matches. However, user dissatisfaction highlights challenges for the algorithms in producing outcomes that align with user preferences. A central issue is understanding which user inputs best reflect user preferences to generate optimal matches. In swipe-based dating applications, swiping functions as a proxy for user preferences, guiding algorithmic recommendations. This study examines how Tinder and Bumble users assess other user profiles, focusing on specific features. Through 80 narrative interviews and grounded theory, findings reveal users assess profiles based on three main features – photos, texts, and music taste – and through single- and multi-feature patterns, with some assessing more consciously than others. By exploring user assessments of algorithmic outcomes in subjectively charged contexts, this study contributes to literature on matching platforms and user assessments of dating profiles, providing insights for refining algorithmic design and fostering user awareness to improve matching outcomes.