Popularity Bias
The systematic overrepresentation of popular items in recommendations, disadvantaging niche items and reinforcing filter bubbles.
Popularity bias systematically favors popular items and disadvantages niches – a central fairness and business problem in RecSys.
Explanation
Popular items have more interaction data → models recommend them more → they become more popular (feedback loop). Countermeasures: inverse propensity scoring, diversity constraints, causal debiasing.
Marketing Relevance
In marketing, popularity bias amplifies bestsellers and neglects long-tail products with often higher margins.
Example
A bookshop only recommends bestsellers, although niche books would be more relevant for the specific user.
Common Pitfalls
Too aggressive debiasing can reduce accuracy. Popularity is sometimes a valid quality signal.
Origin & History
Steck (2011) formalized popularity bias in RecSys. Abdollahpouri et al. (2019) showed its impact on fairness. Causal debiasing (Schnabel et al., 2016) became a standard approach.
Comparisons & Differences
Popularity Bias vs. Filter Bubble
Filter bubble limits diversity for users; popularity bias limits visibility for items.