GroupLens, introduced in 1992, was one of the first systems to base recommendations exclusively on user historical interactions - specifically, explicit ratings of news articles. Its significance is that it established the foundational principle of collaborative filtering: using recorded user behavior rather than item attributes alone to drive recommendations. This approach marked the transition from generic IR outputs to genuinely personalized results. For product teams evaluating recommender infrastructure today, understanding this origin clarifies why behavioral data collection - interaction history, explicit ratings, implicit signals - remains central to recommendation quality.