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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | Journal of Physics: Complexity |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-072X/ad9cdd |
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| _version_ | 1850123222311239680 |
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| author | Giordano De Marzo Pietro Gravino Vittorio Loreto |
| author_facet | Giordano De Marzo Pietro Gravino Vittorio Loreto |
| author_sort | Giordano De Marzo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dbed2f9f44a14ac596440fdb1de82afb |
| institution | OA Journals |
| issn | 2632-072X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Journal of Physics: Complexity |
| spelling | doaj-art-dbed2f9f44a14ac596440fdb1de82afb2025-08-20T02:34:39ZengIOP PublishingJournal of Physics: Complexity2632-072X2024-01-015404500810.1088/2632-072X/ad9cddRecommender systems may enhance the discovery of noveltiesGiordano De Marzo0https://orcid.org/0000-0002-3127-5336Pietro Gravino1https://orcid.org/0000-0002-0937-8830Vittorio Loreto2University of Konstanz , Universitaetstrasse 10, 78457 Konstanz, Germany; Sony Computer Science Laboratories Paris , 6, Rue Amyot, 75005 Paris, France; Centro Ricerche Enrico Fermi , Piazza del Viminale, 1, 00184 Rome, Italy; Complexity Science Hub Vienna , Josefstaedter Strasse 39, 1080 Vienna, AustriaSony Computer Science Laboratories Paris , 6, Rue Amyot, 75005 Paris, France; Sony Computer Science Laboratories Rome , Joint Initiative CREF-SONY, Piazza del Viminale, 1, 00184 Rome, Italy; Centro Ricerche Enrico Fermi , Piazza del Viminale, 1, 00184 Rome, ItalySony Computer Science Laboratories Rome , Joint Initiative CREF-SONY, Piazza del Viminale, 1, 00184 Rome, Italy; Centro Ricerche Enrico Fermi , Piazza del Viminale, 1, 00184 Rome, Italy; Complexity Science Hub Vienna , Josefstaedter Strasse 39, 1080 Vienna, Austria; Sapienza University of Rome , P.le A. Moro, 2, 00185 Rome, ItalyRecommender 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.https://doi.org/10.1088/2632-072X/ad9cddHeaps’ lawrecommendation algorithmsopinion polarizationurn models |
| spellingShingle | Giordano De Marzo Pietro Gravino Vittorio Loreto Recommender systems may enhance the discovery of novelties Journal of Physics: Complexity Heaps’ law recommendation algorithms opinion polarization urn models |
| title | Recommender systems may enhance the discovery of novelties |
| title_full | Recommender systems may enhance the discovery of novelties |
| title_fullStr | Recommender systems may enhance the discovery of novelties |
| title_full_unstemmed | Recommender systems may enhance the discovery of novelties |
| title_short | Recommender systems may enhance the discovery of novelties |
| title_sort | recommender systems may enhance the discovery of novelties |
| topic | Heaps’ law recommendation algorithms opinion polarization urn models |
| url | https://doi.org/10.1088/2632-072X/ad9cdd |
| work_keys_str_mv | AT giordanodemarzo recommendersystemsmayenhancethediscoveryofnovelties AT pietrogravino recommendersystemsmayenhancethediscoveryofnovelties AT vittorioloreto recommendersystemsmayenhancethediscoveryofnovelties |