The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Recommender systems and their impact on sales diversity
Abstract
This paper examines the effect of recommender systems on the diversity of sales. Two anecdotal views exist about such effects. Some believe recommenders help consumers discover new products and thus increase sales diversity. Others believe recommenders only reinforce the popularity of already popular products. This paper is a first attempt to reconcile these seemingly incompatible views. We explore the question in two ways. First, modeling recommender systems analytically allows us to explore their path dependent effects. Second, turning to simulation, we increase the realism of our results by combining choice models with actual implementations of recommender systems. We arrive at three main results. One, some common recommenders lead to a net reduction in average sales diversity. Two, there exists path dependence, and in individual instances the same recommender can either increase or decrease diversity. Three, we show how basic design choices affect the outcome.
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Cites
Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Amazon.com recommendations: item-to-item collaborative filtering
Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing · doi:10.1109/mic.2003.1167344 · Cited by 5,403
Cited by
Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity
Fleder, D., & Hosanagar, K. (2009) · Management Science · doi:10.1287/mnsc.1080.0974 · Cited by 745
Self-citationThe impact of YouTube recommendation system on video views
Zhou, R., Khemmarat, S., & Gao, L. (2010) · Proceedings of the 10th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1879141.1879193 · Cited by 347
Bountouridis, D., Harambam, J., Makhortykh, M., Marrero, M., Tintarev, N., & Hauff, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287583 · Cited by 92
User Simulation for Evaluating Information Access Systems
Balog, K., & Zhai, C. (2024) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000098 · Cited by 52
The Influence of Media Bias on News Recommender Systems
Ruan, Q., Mac Namee, B., & Dong, R. (2023) · Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization · doi:10.1145/3565472.3595619 · Cited by 4
Metadata
- Authors
- Fleder, D. M., & Hosanagar, K.
- Venue
- Proceedings of the 8th ACM conference on Electronic commerce
- Publisher
- ACM
- Published
- 2007-06-11
- Pages
- 192-199
- OpenAlex
- W2048934076
- Cited by
- 167
- References
- 2
- FWCI
- 5.33
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Fleder, D. M., & Hosanagar, K. (2007). Recommender systems and their impact on sales diversity. Proceedings of the 8th ACM conference on Electronic commerce. https://doi.org/10.1145/1250910.1250939
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
