The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility
Open accessAbstract
Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility.
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Cites
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Matrix Factorization Techniques for Recommender Systems
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ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION
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Evaluating collaborative filtering recommender systems
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963772 · Cited by 5,798
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The MovieLens Datasets: History and Context
Harper, F. M., & Konstan, J. A. (2016) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2827872 · Cited by 3,907
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Koren, Y. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401944 · Cited by 3,852
Cited by
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812
Bias and Debias in Recommender System: A Survey and Future Directions
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3564284 · Cited by 741
Causal Intervention for Leveraging Popularity Bias in Recommendation
Zhang, Y., Feng, F., He, X., Wei, T., Song, C., Ling, G., & Zhang, Y. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462875 · Cited by 443
Disentangling User Interest and Conformity for Recommendation with Causal Embedding
Zheng, Y., Gao, C., Li, X., He, X., Li, Y., & Jin, D. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449788 · Cited by 356
“Real-world recommender systems are often trained and updated continuously using real-time user interactions, which forms a feedback loop as illustrated in Figure 1, with training data and test data NOT independent and identically distributed (IID) [13].”
MethodEvolution and impact of bias in human and machine learning algorithm interaction
Sun, W., Nasraoui, O., & Shafto, P. (2020) · PLOS ONE · doi:10.1371/journal.pone.0235502 · Cited by 116
Metadata
- Authors
- Chaney, A. J. B., Stewart, B. M., & Engelhardt, B. E.
- Venue
- Proceedings of the 12th ACM Conference on Recommender Systems
- Publisher
- ACM
- Published
- 2018-09-27
- Pages
- 224-232
- OpenAlex
- W2765564115
- Cited by
- 331
- References
- 58
- FWCI
- 18.14
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Chaney, A. J. B., Stewart, B. M., & Engelhardt, B. E. (2018). How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. Proceedings of the 12th ACM Conference on Recommender Systems. https://doi.org/10.1145/3240323.3240370
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