Work

Recommender systems may enhance the discovery of novelties

Year: 2024

Type: article

Abstract: 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.

Source: Journal of Physics Complexity

DOI: 10.1088/2632-072x/ad9cdd

Authors: Giordano De Marzo, Pietro Gravino, Vittorio Loreto

Institutions: University of Konstanz, Centro Ricerche Enrico Fermi, Complexity Science Hub, Sony (France), Sapienza University of Rome

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,385

Funder: Fondazione Zegna