The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity
Open accessAbstract
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 seeks 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. First, some well-known recommenders can lead to a reduction in sales diversity. Because common recommenders (e.g., collaborative filters) recommend products based on sales and ratings, they cannot recommend products with limited historical data, even if they would be rated favorably. In turn, these recommenders can create a rich-get-richer effect for popular products and vice versa for unpopular ones. This bias toward popularity can prevent what may otherwise be better consumer-product matches. That diversity can decrease is surprising to consumers who express that recommendations have helped them discover new products. In line with this, result two shows that it is possible for individual-level diversity to increase but aggregate diversity to decrease. Recommenders can push each person to new products, but they often push users toward the same products. Third, we show how basic design choices affect the outcome, and thus managers can choose recommender designs that are more consistent with their sales goals and consumers' preferences.
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Cites
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
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Cited by
Goldfarb, A., & Tucker, C. (2019) · Journal of Economic Literature · doi:10.1257/jel.20171452 · Cited by 2,744
Brynjolfsson, E., Hu, Y. (., & Simester, D. (2011) · Management Science · doi:10.1287/mnsc.1110.1371 · Cited by 847
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Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed
Rader, E., & Gray, R. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702174 · Cited by 386
Algorithmic Effects on the Diversity of Consumption on Spotify
Anderson, A., Maystre, L., Anderson, I., Mehrotra, R., & Lalmas, M. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380281 · Cited by 206
“There are many definitions of consumption diversity in the literature, the most common of which are functions of the number of times each item is consumed, such as the Gini coefficient [11, 31] and entropy [7, 24].”
BackgroundMeasuring algorithmically infused societies
Wagner, C., Strohmaier, M., Olteanu, A., Kıcıman, E., Contractor, N., & Eliassi-Rad, T. (2021) · Nature · doi:10.1038/s41586-021-03666-1 · Cited by 144
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Pedreschi, D., Pappalardo, L., Ferragina, E., Baeza-Yates, R., Barabási, A.-L., Dignum, F., Dignum, V., Eliassi-Rad, T., Giannotti, F., Kertész, J., et al. (2025) · Artificial Intelligence · doi:10.1016/j.artint.2024.104244 · Cited by 102
Automated Product Recommendations with Preference-Based Explanations
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The Effect of Recommendations on Network Structure
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How Artificial Intelligence Constrains the Human Experience
Valenzuela, A., Puntoni, S., Hoffman, D., Castelo, N., De Freitas, J., Dietvorst, B., Hildebrand, C., Huh, Y. E., Meyer, R., Sweeney, M. E., et al. (2024) · Journal of the Association for Consumer Research · doi:10.1086/730709 · Cited by 81
User Simulation for Evaluating Information Access Systems
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Metadata
- Authors
- Fleder, D., & Hosanagar, K.
- Venue
- Management Science
- Published
- 2009-05
- Volume
- 55
- Issue
- 5
- Pages
- 697-712
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W3122511740
- Cited by
- 745
- References
- 2
- FWCI
- 83.86
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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