Flore Vancompernolle Vromman
ABSTRACT
By recommending only, or mostly, items close to our supposedly known tastes, a recommender system could lead to the narrowing of the diversity/novelty of the recommended list, leading to the creation of so-called filter bubbles. Authors however disagree on the real existence of filter bubbles and, if real, on the responsibility of algorithms in the process of their creation. This paper aims to experimentally analyze the potential creation of such filter bubbles by testing various collaborative filtering algorithms on their intrinsic propensity of changing the diversity/novelty of recommended items, in configurations where users behave, with various intensities, in a way that could influence the evolution in diversity/novelty. Results show that both human and algorithmic components could have an impact on diversity/novelty, depending on the type of algorithm.
- John S. Breese, David Heckerman, and Carl Kadie. 2013. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. arxiv:1301.7363 [cs.IR]Google Scholar
- Sylvain Castagnos, Armelle Brun, and Anne Boyer. 2014. La diversité: entre besoin et méfiance dans les systèmes de recommandation. Revue I3-Information Interaction Intelligence (2014).Google Scholar
- Daniel Geschke, Jan Lorenz, and Peter Holtz. 2019. The triple-filter bubble: Using agent-based modelling to test a meta-theoretical framework for the emergence of filter bubbles and echo chambers. British Journal of Social Psychology 58, 1 (2019), 129–149.Google ScholarCross Ref
- GroupLens. 2021. MovieLens. https://grouplens.org/datasets/movielens/Google Scholar
- Kartik Hosanagar, Daniel Fleder, Dokyun Lee, and Andreas Buja. 2014. Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation. Management Science 60, 4 (2014), 805–823. http://www.jstor.org/stable/42919571Google ScholarDigital Library
- Marius Kaminskas and Derek Bridge. 2016. Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems (TiiS) 7, 1(2016), 1–42.Google ScholarDigital Library
- Yehuda Koren. 2010. Collaborative Filtering with Temporal Dynamics. Commun. ACM 53 (04 2010), 89–97. https://doi.org/10.1145/1721654.1721677Google Scholar
- N.K. Lathia. 2010. Evaluating collaborative filtering over time. Ph.D. Dissertation. UCL (University College London).Google Scholar
- Tien T. Nguyen, Pik-Mai Hui, F. Maxwell Harper, Loren Terveen, and Joseph A. Konstan. 2014. Exploring the Filter Bubble: The Effect of Using Recommender Systems on Content Diversity(WWW ’14). Association for Computing Machinery, New York, NY, USA, 677–686. https://doi.org/10.1145/2566486.2568012Google Scholar
- Dimitar Nikolov, Diego F. M. Oliveira, Alessandro Flammini, and Filippo Menczer. 2015. Measuring online social bubbles. PeerJ. Computer science 1 (2015), e38.Google Scholar
- Eli Pariser. 2011. The filter bubble: What the Internet is hiding from you. Penguin UK, New York, N.Y.Google ScholarDigital Library
- Robert D. Putnam. 2000. Bowling Alone: The Collapse and Revival of American Community. Simon & Schuster.Google ScholarDigital Library
- KE Stanovich, RF West, and ME Toplak. 2013. Myside bias, rational thinking, and intelligence. Current Directions in Psychological Science 22, 4 (2013), 259–264. https://doi.org/10.1177/0963721413480174Google ScholarCross Ref
- David Sumpter. 2018. Outnumbered: from Facebook and Google to fake news and filter-bubbles - the algorithms that control our lives. Bloomsbury Sigma, London.Google Scholar
- CR Sunstein. 2002. The law of group polarization. Journal of Political Philosophy 10, 2 (2002), 175–195. https://doi.org/10.1111/1467-9760.00148Google ScholarCross Ref
- Cass R. Sunstein. 2007. Republic.com 2.0. Princeton university press, Princeton.Google ScholarDigital Library
- Zoltán Szlávik, Wojtek Kowalczyk, and Martijn Schut. 2011. Diversity measurement of recommender systems under different user choice models. In Fifth International AAAI Conference on Weblogs and Social Media.Google Scholar
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