Recommender systems may enhance the discovery of novelties

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. In this paper we present a model that explores the influence of recommender systems on novel content...

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Bibliographic Details
Published in:Journal of physic, complexity complexity, 2024-12, Vol.5 (4), p.45008
Main Authors: De Marzo, Giordano, Gravino, Pietro, Loreto, Vittorio
Format: Article
Language:English
Subjects:
Heaps’ law
opinion polarization
recommendation algorithms
urn models
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Summary: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. In this paper we present a model that explores the influence of recommender systems on novel content discovery. Surprisingly, analytical and numerical findings reveal that these techniques can enhance novelty discovery rates. Also, distinct algorithms with similar discovery rates yield different outcomes, with the matrix factorization algorithm producing opinion polarization. Our approach shed light on the interplay between algorithmic recommendations and novelties discovery, offering a framework to enhance recommendation techniques beyond accuracy metrics.
ISSN:2632-072X
2632-072X
DOI:10.1088/2632-072X/ad9cdd