Md Sanzeed Anwar
ABSTRACT
Recommendation algorithms play a pivotal role in shaping our media choices, which makes it crucial to comprehend their long-term impact on user behavior. These algorithms are often linked to two critical outcomes: homogenization, wherein users consume similar content despite disparate underlying preferences, and the filter bubble effect, wherein individuals with differing preferences only consume content aligned with their preferences (without much overlap with other users). Prior research assumes a trade-off between homogenization and filter bubble effects and then shows that personalized recommendations mitigate filter bubbles by fostering homogenization. However, because of this assumption of a tradeoff between these two effects, prior work cannot develop a more nuanced view of how recommendation systems may independently impact homogenization and filter bubble effects. We develop a more refined definition of homogenization and the filter bubble effect by decomposing them into two key metrics: how different the average consumption is between users (inter-user diversity) and how varied an individual's consumption is (intra-user diversity). We then use a novel agent-based simulation framework that enables a holistic view of the impact of recommendation systems on homogenization and filter bubble effects. Our simulations show that traditional recommendation algorithms (based on past behavior) mainly reduce filter bubbles by affecting inter-user diversity without significantly impacting intra-user diversity. Building on these findings, we introduce two new recommendation algorithms that take a more nuanced approach by accounting for both types of diversity.
Supplemental Material
- Guy Aridor, Duarte Goncalves, and Shan Sikdar. 2020. Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems. In Proceedings of the 14th ACM Conference on Recommender Systems (Virtual Event, Brazil) (RecSys '20). Association for Computing Machinery, New York, NY, USA, 82--91. https: //doi.org/10.1145/3383313.3412246Google Scholar
Digital Library
- Eytan Bakshy, Solomon Messing, and Lada A. Adamic. 2015. Exposure to ideologically diverse news and opinion on Facebook. Science 348, 6239 (2015), 1130--1132. https://doi.org/10.1126/science.aaa1160 arXiv:https://www.science.org/doi/pdf/10.1126/science.aaa1160Google ScholarCross Ref
- A. Bruns. 2019. Filter bubble. https://doi.org/10.14763/2019.4.1426Google ScholarCross Ref
- Pablo Castells, Neil Hurley, and Saúl Vargas. 2022. Novelty and Diversity in Recommender Systems. Springer US, New York, NY, 603--646. https://doi.org/10. 1007/978--1-0716--2197--4_16Google Scholar
- Allison J. B. Chaney, Brandon M. Stewart, and Barbara E. Engelhardt. 2018. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys '18). Association for Computing Machinery, New York, NY, USA, 224--232. https://doi.org/10.1145/ 3240323.3240370Google Scholar
- Seth Flaxman, Sharad Goel, and Justin M. Rao. 2016. Filter Bubbles, Echo Chambers, and Online News Consumption. Public Opinion Quarterly 80, S1 (03 2016), 298--320. https://doi.org/ 10.1093/poq/nfw006 arXiv:https://academic.oup.com/poq/articlepdf/ 80/S1/298/17120810/nfw006.pdfGoogle ScholarCross Ref
- Daniel Fleder and Kartik Hosanagar. 2009. Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity. , 697--712 pages. https://doi.org/10.1287/mnsc.1080.0974Google ScholarDigital Library
- 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. https://doi.org/10.1111/bjso.12286 arXiv:https://bpspsychub.onlinelibrary.wiley.com/doi/pdf/10.1111/bjso.12286Google ScholarCross Ref
- Kartik Hosanagar, Daniel Fleder, Dokyun Lee, and Andreas Buja. 2013. Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation. Management Science 60(4) (2013), 805--823.Google Scholar
- Jonas Kaiser and Adrian Rauchfleisch. 2020. Birds of a Feather Get Recommended Together: Algorithmic Homophily in YouTube's Channel Recommendations in the United States and Germany. Social Media Society 6, 4 (2020), 2056305120969914. https://doi.org/10.1177/2056305120969914 arXiv:https://doi.org/10.1177/2056305120969914Google ScholarCross Ref
- Matevz Kunaver and Tomaz Pozrl. 2017. Diversity in recommender systems -- A survey. Knowledge-Based Systems 123 (2017), 154--162. https://doi.org/10.1016/j. knosys.2017.02.009Google ScholarDigital Library
- Anja Lambrecht and Catherine Tucker. 2016. Algorithmic Bias? An Empirical Study into Apparent Gender-Based Discrimination in the Display of STEM Career Ads. SSRN Electronic Journal (01 2016). https://doi.org/10.2139/ssrn.2852260Google ScholarCross Ref
- Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, and Robin Burke. 2020. Feedback Loop and Bias Amplification in Recommender Systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (Virtual Event, Ireland) (CIKM '20). Association for Computing Machinery, New York, NY, USA, 2145--2148. https://doi.org/10.1145/3340531.3412152Google ScholarDigital Library
- Kari Karppinen Natali Helberger and Lucia D'Acunto. 2018. Exposure diversity as a design principle for recommender systems. Information, Communication & Society 21, 2 (2018), 191--207. https://doi.org/10.1080/1369118X.2016.1271900 arXiv:https://doi.org/10.1080/1369118X.2016.1271900Google ScholarCross Ref
- 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. In Proceedings of the 23rd International Conference on World Wide Web (Seoul, Korea) (WWW '14). Association for Computing Machinery, New York, NY, USA, 677--686. https://doi.org/10.1145/2566486.2568012Google ScholarDigital Library
- Derek O'Callaghan, Derek Greene, Maura Conway, Joe Carthy, and Pádraig Cunningham. 2015. Down the (White) Rabbit Hole: The Extreme Right and Online Recommender Systems. Social Science Computer Review 33, 4 (2015), 459--478. https://doi.org/10.1177/0894439314555329 arXiv:https://doi.org/10.1177/0894439314555329Google ScholarDigital Library
- Eli Pariser. 2011. The filter bubble: How the new personalized web is changing what we read and how we think. Penguin.Google ScholarDigital Library
- Matthew J. Salganik, Peter Sheridan Dodds, and Duncan J. Watts. 2006. Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market. Science 311, 5762 (2006), 854--856. https://doi.org/10.1126/science.1121066 arXiv:https://www.science.org/doi/pdf/10.1126/science.1121066Google ScholarCross Ref
- Jessica Su, Aneesh Sharma, and Sharad Goel. 2016. The Effect of Recommendations on Network Structure. In Proceedings of the 25th International Conference on World Wide Web (Montréal, Québec, Canada) (WWW '16). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1157--1167. https://doi.org/10.1145/2872427.2883040Google ScholarDigital Library
Index Terms
- Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns
Recommendations
Exploring the filter bubble: the effect of using recommender systems on content diversity
WWW '14: Proceedings of the 23rd international conference on World wide webEli Pariser coined the term 'filter bubble' to describe the potential for online personalization to effectively isolate people from a diversity of viewpoints or content. Online recommender systems - built on algorithms that attempt to predict which ...
Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems
RecSys '20: Proceedings of the 14th ACM Conference on Recommender SystemsWe study a model of user decision-making in the context of recommender systems via numerical simulation. Our model provides an explanation for the findings of Nguyen, et. al (2014), where, in environments where recommender systems are typically ...
A framework for diversifying recommendation lists by user interest expansion
Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N ...
Comments