In domains where users interact infrequently - such as job platforms or professional networks - interaction history alone is insufficient to build a reliable preference model. Background attributes like skills, interests, or professional bio provide a non-behavioral signal that compensates for sparse interaction data, enabling relevant recommendations from a user's first session. This is especially consequential for new user retention: a platform that can deliver relevant results before a user has clicked or purchased is more likely to establish a habit. Organizations in low-frequency domains should prioritize collecting structured background attributes during onboarding rather than relying solely on behavioral data accumulation.