  Recommender systems are vital for shaping user online experiences. While some
believe they may limit new content exploration and promote opinion
polarization, a systematic analysis is still lacking. We present a model that
explores the influence of recommender systems on novel content discovery.
Surprisingly, analytical and numerical findings reveal these techniques can
enhance novelty discovery rates. Also, distinct algorithms with similar
discovery rates yield varying opinion polarization outcomes. Our approach
offers a framework to enhance recommendation techniques beyond accuracy
metrics.
