Recommender Popularity Bias: The Rich-Get-Richer Feedback Loop

Recommendation systems don't just reflect what users like — the act of recommending changes what gets seen, which changes what gets interacted with, which changes what gets recommended next. This simulator makes that closed loop visible: 72 catalogue items sit on a 3D stage as bars whose height and colour track their cumulative exposure, and every round a collaborative-filtering-style rule picks K items to "recommend" with probability proportional to a tunable power of their exposure so far. Push the popularity bias α up and watch a handful of bars pull away into a spike while most of the catalogue stalls at its starting seed; raise the exploration rate ε and watch the distribution stay flat instead. Live Gini coefficient, top-10% exposure share and long-tail percentage quantify exactly how concentrated the catalogue has become, the same metrics used to audit real production recommenders for popularity bias.