L. Elisa Celis
ABSTRACT
Personalization is pervasive in the online space as it leads to higher efficiency for the user and higher revenue for the platform by individualizing the most relevant content for each user. However, recent studies suggest that such personalization can learn and propagate systemic biases and polarize opinions; this has led to calls for regulatory mechanisms and algorithms that are constrained to combat bias and the resulting echo-chamber effect. We propose a versatile framework that allows for the possibility to reduce polarization in personalized systems by allowing the user to constrain the distribution from which content is selected. We then present a scalable algorithm with provable guarantees that satisfies the given constraints on the types of the content that can be displayed to a user, but -- subject to these constraints -- will continue to learn and personalize the content in order to maximize utility. We illustrate this framework on a curated dataset of online news articles that are conservative or liberal, show that it can control polarization, and examine the trade-off between decreasing polarization and the resulting loss to revenue. We further exhibit the flexibility and scalability of our approach by framing the problem in terms of the more general diverse content selection problem and test it empirically on both a News dataset and the MovieLens dataset.
- {n. d.}. AllSides Media Bias Ratings. https:/www.allsides.com/media-bias/media-bias-ratings.Google Scholar
- {n. d.}. Webhose News API. https://webhose.io/data-feeds/news-api/.Google Scholar
- Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári. 2011. Improved Algorithms for Linear Stochastic Bandits. In Advances In Neural Information Processing Systems. Google ScholarDigital Library
- Gediminas Adomavicius, Jesse Bockstedt, Curley Shawn, and Jingjing Zhang. 2014. De-biasing user preference ratings in recommender systems. In Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, Co-located with ACM Conference on Recommender Systems.Google Scholar
- Gediminas Adomavicius and YoungOk Kwon. 2012. Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering 24, 5 (2012), 896--911. Google ScholarDigital Library
- Shipra Agrawal and Nikhil Devanur. 2016. Linear Contextual Bandits with Knapsacks. In Advances In Neural Information Processing Systems. 3450--3458. Google ScholarDigital Library
- Shipra Agrawal and Navin Goyal. 2012. Analysis of thompson sampling for the multi-armed bandit problem. In Conference on Learning Theory. 39--1.Google Scholar
- Rubayyi Alghamdi and Khalid Alfalqi. 2015. A survey of topic modeling in text mining. Int. J. Adv. Comput. Sci. Appl.(IJACSA) 6, 1 (2015).Google Scholar
- Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. 2002. Finite-time analysis of the multiarmed bandit problem. Machine learning 47, 2-3 (2002), 235--256. Google ScholarDigital Library
- Drake Baer. 2016. The 'Filter Bubble' Explains Why Trump Won and You Didn't See It Coming. NY Mag.Google Scholar
- Engin Bozdag and Jeroen van den Hoven. 2015. Breaking the filter bubble: democracy and design. Ethics and Information Technology 17, 4 (01 Dec 2015), 249--265. Google ScholarDigital Library
- L. E. Celis, L. Huang, V. Keswani, and N. K. Vishnoi. 2018. Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees. ArXiv e-prints (June 2018). arXiv:1806.06055Google Scholar
- Michael Conover, Jacob Ratkiewicz, Matthew R Francisco, Bruno Gonçalves, Filippo Menczer, and Alessandro Flammini. 2011. Political polarization on twitter. ICWSM 133 (2011), 89--96.Google Scholar
- Varsha Dani, Thomas P Hayes, and Sham M Kakade. 2008. Stochastic Linear Optimization under Bandit Feedback. In Proceedings of the Annual Conference on Learning Theory (COLT).Google Scholar
- Amit Datta, Michael Carl Tschantz, and Anupam Datta. 2015. Automated experiments on ad privacy settings. Proceedings on Privacy Enhancing Technologies 2015, 1 (2015), 92--112.Google ScholarCross Ref
- Wenkui Ding, Tao Qin, Xu-Dong Zhang, and Tie-Yan Liu. 2013. Multi-Armed Bandit with Budget Constraint and Variable Costs.. In AAAI. Google ScholarDigital Library
- Robert Epstein and Ronald E Robertson. 2015. The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections. Proceedings of the National Academy of Sciences 112, 33 (2015), E4512--E4521.Google ScholarCross Ref
- Ayman Farahat and Michael C Bailey. 2012. How effective is targeted advertising?. In Proceedings of the 21st international conference on World Wide Web. ACM. Google ScholarDigital Library
- Thomas Fox-Brewster. 2017. Creepy Or Cool? Twitter Is Tracking Where You've Been, What You Like And Is Telling Advertisers. Forbes Magazine.Google Scholar
- Venkata Rama Kiran Garimella and Ingmar Weber. 2017. A Long-Term Analysis of Polarization on Twitter. In ICWSM.Google Scholar
- Avi Goldfarb and Catherine Tucker. 2011. Online display advertising: Targeting and obtrusiveness. Marketing Science (2011). Google ScholarDigital Library
- F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. 5, 4, Article 19 (Dec. 2015), 19 pages. Google ScholarDigital Library
- Sounman Hong and Sun Hyoung Kim. 2016. Political polarization on twitter: Implications for the use of social media in digital governments. Government Information Quarterly 33, 4 (2016), 777--782.Google ScholarCross Ref
- Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth. 2016. Fairness in learning: Classic and contextual bandits. In Advances in Neural Information Processing Systems. 325--333. Google ScholarDigital Library
- Lihong Li, Wei Chu, John Langford, and Robert E Schapire. 2010. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web. ACM, 661--670. Google ScholarDigital Library
- Shuai Li, Alexandras Karatzoglou, and Claudio Gentile. 2016. Collaborative filtering bandits. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 539--548. Google ScholarDigital Library
- Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen. 2010. Personalized news recommendation based on click behavior. In Proceedings of the 15th international conference on Intelligent user interfaces. ACM. Google ScholarDigital Library
- Sandeep Pandey and Christopher Olston. 2006. Handling Advertisements of Unknown Quality in Search Advertising. In Advances in Neural Information Processing Systems. Google ScholarDigital Library
- Eli Pariser. 2011. The filter bubble: What the Internet is hiding from you. Penguin UK. Google ScholarDigital Library
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830. Google ScholarDigital Library
- Filipe N. Ribeiro, Lucas Henrique, Fabricio Benevenuto, Abhijnan Chakraborty, Juhi Kulshrestha, Mahmoudreza Babaei, and Krishna P. Gummadi. 2018. Media Bias Monitor: Quantifying Biases of Social Media News Outlets at Scale. In Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM).Google Scholar
- Ronald E. Robertson, David Lazer, and Christo Wilson. 2018. Auditing the Personalization and Composition of Politically-Related Search Engine Results Pages. In Proceedings of the 2018 World Wide Web Conference. Google ScholarDigital Library
- Peter J. Rousseeuw. 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. (1987). Google ScholarDigital Library
- Pranav Sakulkar and Bhaskar Krishnamachari. 2016. Stochastic contextual bandits with known reward functions. arXiv preprint arXiv:1605.00176 (2016).Google Scholar
- Till Speicher, Muhammad Ali, Giridhari Venkatadri, Filipe Nunes Ribeiro, George Arvanitakis, Fabricio Benevenuto, Krishna P. Gummadi, Patrick Loiseau, and Alan Mislove. 2018. Potential for Discrimination in Online Targeted Advertising. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency. PMLR.Google Scholar
- Karthik Sridharan. 2002. A gentle introduction to concentration inequalities. (2002).Google Scholar
- Chong Wang and David M Blei. 2011. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 448--456. Google ScholarDigital Library
- Jacek Wasilewski and Neil Hurley. 2016. Incorporating Diversity in a Learning to Rank Recommender System.. In FLAIRS Conference. 572--578.Google Scholar
- Ingmar Weber, Venkata R Kiran Garimella, and Alaa Batayneh. 2013. Secular vs. islamist polarization in egypt on twitter. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ACM. Google ScholarDigital Library
- Yiming Yan. {n.d.}. Mehrotra's Predictor-Corrector Interior Point Method. https://github.com/YimingYAN/mpc.Google Scholar
- Yi Zhang. 2005. Bayesian Graphical Models For Adaptive Filtering. In PhD Thesis. Google ScholarDigital Library
Index Terms
- Controlling Polarization in Personalization: An Algorithmic Framework
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