research-article
Free access
Authors: Jerry Chee, Shankar Kalyanaraman, Sindhu Kiranmai Ernala, Udi Weinsberg, Sarah Dean, Stratis Ioannidis
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Pages 255 - 265
Published: 24 August 2024 Publication History
Metrics
Total Citations1Total Downloads15Last 12 Months15
Last 6 weeks15
New Citation Alert added!
This alert has been successfully added and will be sent to:
You will be notified whenever a record that you have chosen has been cited.
To manage your alert preferences, click on the button below.
Manage my Alerts
New Citation Alert!
Please log in to your account
PDFeReader
- View Options
- References
- Media
- Tables
- Share
Abstract
We consider a recommender system that takes into account the interplay between recommendations, the evolution of user interests, and harmful content. We model the impact of recommendations on user behavior, particularly the tendency to consume harmful content. We seek recommendation policies that establish a tradeoff between maximizing click-through rate (CTR) and mitigating harm. We establish conditions under which the user profile dynamics have a stationary point, and propose algorithms for finding an optimal recommendation policy at stationarity. We experiment on a semi-synthetic movie recommendation setting initialized with real data and observe that our policies outperform baselines at simultaneously maximizing CTR and mitigating harm.
Supplemental Material
M4V File - rtp1522-video
Harm Mitigation in Recommender Systems under User Preference Dynamics - 2 minute video
- Download
- 17.67 MB
References
[1]
S. Agrawal, V. Avadhanula, V. Goyal, and A. Zeevi. Thompson sampling for the mnl-bandit. In S. Kale and O. Shamir, editors, Proceedings of the 2017 Conference on Learning Theory, volume 65 of Proceedings of Machine Learning Research, pages 76--78. PMLR, 07--10 Jul 2017. URL https://proceedings.mlr.press/v65/agrawal17a.html.
[2]
A. Arora, P. Nakov, M. Hardalov, S. M. Sarwar, V. Nayak, Y. Dinkov, D. Zlatkova, K. Dent, A. Bhatawdekar, G. Bouchard, et al. Detecting harmful content on online platforms: what platforms need vs. where research efforts go. ACM Computing Surveys, 56(3):1--17, 2023.
Digital Library
[3]
H. Ashton and M. Franklin. The problem of behaviour and preference manipulation in ai systems. In CEUR Workshop Proceedings, volume 3087. CEUR Workshop Proceedings, 2022.
[4]
S. Banach. Sur les opérations dans les ensembles abstraits et leur application aux équations intégrales. Fundamenta mathematicae, 3(1):133--181, 1922.
[5]
S. Beckers, H. Chockler, and J. Y. Halpern. Causal analysis of harm. Advances in Neural Information Processing Systems, 36, 2022.
[6]
M. Benaïm. Dynamics of stochastic approximation algorithms. In Seminaire de probabilites XXXIII, pages 1--68. Springer, 2006.
[7]
A. Benveniste, M. Métivier, and P. Priouret. Adaptive algorithms and stochastic approximations, volume 22. Springer Science & Business Media, 2012.
Digital Library
[8]
D. P. Bertsekas. Nonlinear programming. Journal of the Operational Research Society, 48(3):334--334, 1997.
[9]
M. Blondel, Q. Berthet, M. Cuturi, R. Frostig, S. Hoyer, F. Llinares-López, F. Pedregosa, and J.-P. Vert. Efficient and modular implicit differentiation. Advances in neural information processing systems, 35:5230--5242, 2022.
[10]
V. S. Borkar. Stochastic approximation: a dynamical systems viewpoint, volume 48. Springer, 2009.
[11]
M. Carroll, D. Hadfield-Menell, S. Russell, and A. Dragan. Estimating and penalizing preference shift in recommender systems. In Fifteenth ACM Conference on Recommender Systems, pages 661--667, 2021.
Digital Library
[12]
I. H. Center. Imdb parental guide, 2023. https://help.imdb.com/article/contribution/titles/parental-guide/GF4KYKYJA4PKQB32.
[13]
B. H. Chaptini. Use of discrete choice models with recommender systems. PhD thesis, Massachusetts Institute of Technology, 2005.
Digital Library
[14]
J. Chee, S. Kalyanaraman, S. K. Ernala, U. Weinsberg, S. Dean, and S. Ioannidis. Harm mitigation in recommender systems under user preference dynamics, 2024. Arxiv http://arxiv.org/abs/2406.09882.
Digital Library
[15]
P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the fourth ACM conference on Recommender systems, pages 39--46, 2010.
Digital Library
[16]
M. Curmei, A. A. Haupt, B. Recht, and D. Hadfield-Menell. Towards psychologically-grounded dynamic preference models. In Proceedings of the 16th ACM Conference on Recommender Systems, pages 35--48, 2022.
Digital Library
[17]
M. Danaf, F. Becker, X. Song, B. Atasoy, and M. Ben-Akiva. Online discrete choice models: Applications in personalized recommendations. Decision Support Systems, 119:35--45, 2019.
Digital Library
[18]
S. Dean and J. Morgenstern. Preference dynamics under personalized recommendations. arXiv preprint arXiv:2205.13026, 2022.
[19]
M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS), 22(1):143--177, 2004.
[20]
F. Fabbri, Y. Wang, F. Bonchi, C. Castillo, and M. Mathioudakis. Rewiring what-to-watch-next recommendations to reduce radicalization pathways. In Proceedings of the ACM Web Conference 2022, pages 2719--2728, 2022.
Digital Library
[21]
D. Fleder and K. Hosanagar. Blockbuster culture's next rise or fall: The impact of recommender systems on sales diversity. Management science, 55(5):697--712, 2009.
Digital Library
[22]
G. Folland. Avanced Calculus. Pearson, 2001.
[23]
Y. Ge, S. Zhao, H. Zhou, C. Pei, F. Sun, W. Ou, and Y. Zhang. Understanding echo chambers in e-commerce recommender systems. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pages 2261--2270, 2020.
Digital Library
[24]
T. Gillespie. Content moderation, ai, and the question of scale. Big Data & Society, 7(2):2053951720943234, 2020.
[25]
R. Gormann and S. Armstrong. The dangers in algorithms learning humans' values and irrationalities. arXiv preprint arXiv:2202.13985, 2022.
[26]
B. Hayworth. Imdb parental guide, 2023. https://www.kaggle.com/datasets/barryhaworth/imdb-parental-guide.
[27]
N. Hazrati and F. Ricci. Recommender systems effect on the evolution of users' choices distribution. Information Processing & Management, 59(1):102766, 2022.
Digital Library
[28]
S. Hosseini, H. Palangi, and A. H. Awadallah. An empirical study of metrics to measure representational harms in pre-trained language models. arXiv preprint arXiv:2301.09211, 2023.
[29]
Y. Hou, D. Xiong, T. Jiang, L. Song, and Q. Wang. Social media addiction: Its impact, mediation, and intervention. Cyberpsychology: Journal of psychosocial research on cyberspace, 13(1), 2019.
[30]
F. Huszár, S. I. Ktena, C. O'Brien, L. Belli, A. Schlaikjer, and M. Hardt. Algorithmic amplification of politics on twitter. Proceedings of the National Academy of Sciences, 119(1):e2025334119, 2022.
[31]
D. Jannach. Multi-objective recommender systems: Survey and challenges. arXiv preprint arXiv:2210.10309, 2022.
[32]
H. Jiang, X. Qi, and H. Sun. Choice-based recommender systems: a unified approach to achieving relevancy and diversity. Operations Research, 62(5):973--993, 2014.
Digital Library
[33]
R. Jiang, S. Chiappa, T. Lattimore, A. György, and P. Kohli. Degenerate feedback loops in recommender systems. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pages 383--390, 2019.
Digital Library
[34]
D. Kalimeris, S. Bhagat, S. Kalyanaraman, and U. Weinsberg. Preference amplification in recommender systems. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 805--815, 2021.
Digital Library
[35]
G. Karypis. Evaluation of item-based top-n recommendation algorithms. In Proceedings of the tenth international conference on Information and knowledge management, pages 247--254, 2001.
Digital Library
[36]
D. Koutra, A. Dighe, S. Bhagat, U. Weinsberg, S. Ioannidis, C. Faloutsos, and J. Bolot. PNP: Fast path ensemble method for movie design. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1527--1536, 2017.
Digital Library
[37]
D. Kraft. A software package for sequential quadratic programming. Forschungsbericht- Deutsche Forschungs- und Versuchsanstalt fur Luft- und Raumfahrt, 1988.
[38]
K. Krauth, S. Dean, A. Zhao, W. Guo, M. Curmei, B. Recht, and M. I. Jordan. Do offline metrics predict online performance in recommender systems? arXiv preprint arXiv:2011.07931, 2020.
[39]
A. Lada, M. Wang, and T. Yan. How machine learning powers Facebook's News Feed ranking algorithm, 2021. https://engineering.fb.com/2021/01/26/core-infra/news-feed-ranking/.
[40]
M. Ledwich, A. Zaitsev, and A. Laukemper. Radical bubbles on youtube? revisiting algorithmic extremism with personalised recommendations. First Monday, 2022.
[41]
W. Lee, S. S. Lee, S. Chung, and D. An. Harmful contents classification using the harmful word filtering and svm. In Computational Science--ICCS 2007: 7th International Conference, Beijing, China, May 27--30, 2007, Proceedings, Part III 7, pages 18--25. Springer, 2007.
[42]
N. E. Leonard, K. Lipsitz, A. Bizyaeva, A. Franci, and Y. Lelkes. The nonlinear feedback dynamics of asymmetric political polarization. Proceedings of the National Academy of Sciences, 118(50):e2102149118, 2021.
[43]
S. A. Levin, H. V. Milner, and C. Perrings. The dynamics of political polarization, 2021.
[44]
L. Y. Lin, J. E. Sidani, A. Shensa, A. Radovic, E. Miller, J. B. Colditz, B. L. Hoffman, L. M. Giles, and B. A. Primack. Association between social media use and depression among us young adults. Depression and anxiety, 33(4):323--331, 2016.
[45]
W. Lu, S. Ioannidis, S. Bhagat, and L. V. Lakshmanan. Optimal recommendations under attraction, aversion, and social influence. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 811--820, 2014.
Digital Library
[46]
R. D. Luce. Individual choice behavior: A theoretical analysis, 1959.
[47]
S. Maniu, S. Ioannidis, and B. Cautis. Bandits under the influence. In 2020 IEEE International Conference on Data Mining (ICDM), pages 1172--1177. IEEE, 2020.
[48]
M. Mansoury, H. Abdollahpouri, M. Pechenizkiy, B. Mobasher, and R. Burke. Feedback loop and bias amplification in recommender systems. In Proceedings of the 29th ACM international conference on information & knowledge management, pages 2145--2148, 2020.
Digital Library
[49]
D. McFadden et al. Conditional logit analysis of qualitative choice behavior. 1973.
[50]
C. Michelot. A Finite Algorithm for Finding the Projection of a Point onto the Canonical Simplex of Rn. Journal of Optimization Theory and Applications, 50(1): 195--200, 1986.
Digital Library
[51]
M.-h. Oh and G. Iyengar. Thompson sampling for multinomial logit contextual bandits. Advances in Neural Information Processing Systems, 32, 2019.
[52]
M.-h. Oh and G. Iyengar. Multinomial logit contextual bandits: Provable optimality and practicality. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 9205--9213, 2021.
[53]
N. Pagan, J. Baumann, E. Elokda, G. De Pasquale, S. Bolognani, and A. Hannák. A classification of feedback loops and their relation to biases in automated decision- making systems. In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, pages 1--14, 2023.
Digital Library
[54]
N. Perra and L. E. Rocha. Modelling opinion dynamics in the age of algorithmic personalisation. Scientific reports, 9(1):7261, 2019.
[55]
F. Pierri, L. Luceri, E. Chen, and E. Ferrara. How does twitter account moderation work? dynamics of account creation and suspension on twitter during major geopolitical events. EPJ Data Science, 12(1):43, 2023.
[56]
N. J. Restrepo, L. Illari, R. Leahy, R. F. Sear, Y. Lupu, and N. F. Johnson. How social media machinery pulled mainstream parenting communities closer to extremes and their misinformation during covid-19. IEEE Access, 2021.
[57]
M. H. Ribeiro, R. Ottoni, R. West, V. A. Almeida, and W. Meira Jr. Auditing radicalization pathways on youtube. In Proceedings of the 2020 conference on fairness, accountability, and transparency, pages 131--141, 2020.
Digital Library
[58]
J. G. Richens, R. Beard, and D. H. Thompson. Counterfactual harm. Advances in Neural Information Processing Systems, 36, 2022.
[59]
W. S. Rossi, J. W. Polderman, and P. Frasca. The closed loop between opinion formation and personalized recommendations. IEEE Transactions on Control of Network Systems, 9(3):1092--1103, 2021.
[60]
R. Shelby, S. Rismani, K. Henne, A. Moon, N. Rostamzadeh, P. Nicholas, N. Yilla, J. Gallegos, A. Smart, E. Garcia, et al. Sociotechnical harms: Scoping a taxonomy for harm reduction. arXiv preprint arXiv:2210.05791, 2022.
[61]
A. Singh, Y. Halpern, N. Thain, K. Christakopoulou, E. Chi, J. Chen, and A. Beutel. Building healthy recommendation sequences for everyone: A safe reinforcement learning approach. In FAccTRec Workshop, 2020.
[62]
J. J. Smith, L. Jayne, and R. Burke. Recommender systems and algorithmic hate. In Proceedings of the 16th ACM Conference on Recommender Systems, pages 592--597, 2022.
Digital Library
[63]
W. Suna and O. Nasraouia. User polarization aware matrix factorization for recommendation systems. 2021.
[64]
Ö. Sürer, R. Burke, and E. C. Malthouse. Multistakeholder recommendation with provider constraints. In Proceedings of the 12th ACM Conference on Recommender Systems, pages 54--62, 2018.
[65]
B. Tabibian, V. Gomez, A. De, B. Schölkopf, and M. G. Rodriguez. On the design of consequential ranking algorithms. In Conference on Uncertainty in Artificial Intelligence, pages 171--180. PMLR, 2020.
[66]
S. Vargas and P. Castells. Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems, pages 109--116, 2011.
Digital Library
[67]
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, I. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, and SciPy 1.0 Contributors. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17:261--272, 2020.
[68]
J. Whittaker, S. Looney, A. Reed, and F. Votta. Recommender systems and the amplification of extremist content. Internet Policy Review, 10(2):1--29, 2021.
[69]
Y. Wu, N. Yang, and H. Luo. Unified group recommendation towards multiple criteria. In Web and Big Data: Third International Joint Conference, APWeb-WAIM 2019, Chengdu, China, August 1--3, 2019, Proceedings, Part II 3, pages 137--151. Springer, 2019.
[70]
L. Xiao, Z. Min, Z. Yongfeng, G. Zhaoquan, L. Yiqun, and M. Shaoping. Fairness-aware group recommendation with pareto-efficiency. In Proceedings of the eleventh ACM conference on recommender systems, pages 107--115, 2017.
Digital Library
[71]
S.-H. Yang, B. Long, A. J. Smola, H. Zha, and Z. Zheng. Collaborative competitive filtering: learning recommender using context of user choice. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pages 295--304, 2011.
Digital Library
[72]
M. Zampieri, S. Rosenthal, P. Nakov, A. Dmonte, and T. Ranasinghe. Offenseval 2023: Offensive language identification in the age of large language models. Natural Language Engineering, 29(6):1416--1435, 2023.
[73]
L. Zhang, T. Yang, Z.-H. Zhou, et al. Dynamic regret of strongly adaptive methods. In International conference on machine learning, pages 5882--5891. PMLR, 2018.
[74]
Y. Zhang, F. Chen, and J. Lukito. Network amplification of politicized information and misinformation about covid-19 by conservative media and partisan influencers on twitter. Political Communication, pages 1--24, 2022.
[75]
Y. Zheng and D. X. Wang. A survey of recommender systems with multi-objective optimization. Neurocomputing, 474:141--153, 2022.
Digital Library
[76]
M. Zinkevich. Online convex programming and generalized infinitesimal gradient ascent. In Proceedings of the 20th international conference on machine learning (icml-03), pages 928--936, 2003.
Digital Library
Cited By
View all
- Chee JKalyanaraman SErnala SWeinsberg UDean SIoannidis SBaeza-Yates RBonchi F(2024)Harm Mitigation in Recommender Systems under User Preference DynamicsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671925(255-265)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671925
Index Terms
Harm Mitigation in Recommender Systems under User Preference Dynamics
Human-centered computing
Collaborative and social computing
Collaborative and social computing theory, concepts and paradigms
Social media
Information systems
Information retrieval
Retrieval tasks and goals
Recommender systems
Recommendations
- Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03
Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...
Read More
- User Personality and User Satisfaction with Recommender Systems
In this study, we show that individual users' preferences for the level of diversity, popularity, and serendipity in recommendation lists cannot be inferred from their ratings alone. We demonstrate that we can extract strong signals about individual ...
Read More
- Investigating serendipity in recommender systems based on real user feedback
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing
Over the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-...
Read More
Comments
Information & Contributors
Information
Published In
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 2024
6901 pages
ISBN:9798400704901
DOI:10.1145/3637528
- General Chairs:
- Ricardo Baeza-Yates
Northeastern University, USA
, - Francesco Bonchi
CENTAI / Eurecat, Italy
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [emailprotected].
Sponsors
- SIGMOD: ACM Special Interest Group on Management of Data
- SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 August 2024
Permissions
Request permissions for this article.
Check for updates
Author Tags
- amplification
- harm mitigation
- recommender systems
- user preference modeling
Qualifiers
- Research-article
Funding Sources
Conference
KDD '24
Sponsor:
- SIGMOD
- SIGKDD
KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 25 - 29, 2024
Barcelona, Spain
Acceptance Rates
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%
Contributors
Other Metrics
View Article Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations
1
Total Citations
15
Total Downloads
- Downloads (Last 12 months)15
- Downloads (Last 6 weeks)15
Reflects downloads up to 29 Aug 2024
Other Metrics
View Author Metrics
Citations
Cited By
View all
- Chee JKalyanaraman SErnala SWeinsberg UDean SIoannidis SBaeza-Yates RBonchi F(2024)Harm Mitigation in Recommender Systems under User Preference DynamicsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671925(255-265)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671925
View Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderGet Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in
Full Access
Get this Publication
Media
Figures
Other
Tables