View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Archive Ouverte en Sciences de l'Information et de la Communication Community-based Recommendations on Twitter: Avoiding The Filter Bubble Quentin Grossetti, Cédric Du Mouza, Nicolas Travers To cite this version: Quentin Grossetti, Cédric Du Mouza, Nicolas Travers. Community-based Recommendations on Twit- ter: Avoiding The Filter Bubble. Web Information Systems Engineering – WISE 2019, Nov 2019, Hong-Kong, China. 10.1007/978-3-030-34223-4_14. hal-02465790 HAL Id: hal-02465790 https://hal-cnam.archives-ouvertes.fr/hal-02465790 Submitted on 4 Feb 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Community-based Recommendations on Twitter: Avoiding The Filter Bubble Quentin Grossetti1, C´edricdu Mouza1, and Nicolas Travers1;2 1 CEDRIC Lab, CNAM Paris, France
[email protected] 2 L´eonardde Vinci P^oleUniversitaire, Research Center, Paris La D´efense,France
[email protected] Abstract. Due to their success, social network platforms are consid- ered today as a major communication mean. In order to increase user engagement, they rely on recommender systems to personalize individual experience by filtering messages according to user interest and/or neigh- borhood.