This capstone report analyzes how user-generated metadata can enhance findability in social software applications like content tagging and recommender systems. The report examines these systems' strengths and weaknesses for information classification, retrieval, and discovery. Based on an analysis of six system-factor combinations, the report finds that content tagging systems have stronger overall findability than recommender systems, particularly for information classification. However, recommender systems exhibit strengths for information discovery. The report provides examples of content tagging and recommender systems to illustrate different design approaches.