Topic modeling is used to provide content-based recommendations for exploring TED resources. Non-negative matrix factorization extracts meaningful topics from speech transcripts without prior knowledge. It calculates the importance of words for each topic and assigns topics scores to summarize documents. An app was created to recommend talks based on extracted topics like education, music, energy, and technology.