Diversity in Recommendations
Strategies for increasing variety in recommendation lists to avoid filter bubbles and improve user satisfaction.
Diversity in recommendations prevents filter bubbles and increases long-term engagement through varied, surprising suggestions.
Explanation
Diversity methods include MMR (Maximal Marginal Relevance), DPP (Determinantal Point Processes), coverage constraints, and serendipity metrics. Trade-off between relevance and diversity.
Marketing Relevance
Diverse recommendations increase long-term engagement, cross-selling, and prevent monotony-driven churn.
Example
Spotify's "Discover Weekly" mixes known genres with surprising discoveries – diversity is a feature, not a bug.
Common Pitfalls
Too much diversity reduces short-term CTR. Diversity metrics don't always correlate with user satisfaction.
Origin & History
Carbonell & Goldstein (1998) introduced MMR. Ziegler et al. (2005) showed diversity increases user satisfaction. DPP-based methods (Chen et al., 2018) became popular for efficient diversification.
Comparisons & Differences
Diversity in Recommendations vs. Popularity Bias
Popularity bias is the problem (too little variety); diversity strategies are the solution.