Big Data and Recommender Systems – Filter Bubbles

Computer ScienceData, AI & Machine LearningAges 17–18

The top half shows the big data pipeline: raw records are collected, then cleaned (removing duplicates, missing fields and badly formatted entries) before analysis; students drag the record count and the cleaning level to see how the number of usable records and the result's reliability change. The bottom half runs a recommender system round by round: it keeps suggesting the topic you watch most, your interest in it grows, diversity measured by entropy keeps dropping and a filter bubble forms; raising the exploration rate breaks the bubble open.