The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Performance of recommender algorithms on top-n recommendation tasks
Abstract
In many commercial systems, the 'best bet' recommendations are shown, but the predicted rating values are not. This is usually referred to as a top-N recommendation task, where the goal of the recommender system is to find a few specific items which are supposed to be most appealing to the user. Common methodologies based on error metrics (such as RMSE) are not a natural fit for evaluating the top-N recommendation task. Rather, top-N performance can be directly measured by alternative methodologies based on accuracy metrics (such as precision/recall).
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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
The long tail: why the future of business is selling less of more
(2007) · Choice Reviews Online · doi:10.5860/choice.44-2783 · Cited by 2,869
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Kaminskas, M., & Bridge, D. (2017) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2926720 · Cited by 465
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LCARS: a location-content-aware recommender system
Yin, H., Sun, Y., Cui, B., Hu, Z., & Chen, L. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487608 · Cited by 331
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility
Chaney, A. J. B., Stewart, B. M., & Engelhardt, B. E. (2018) · Proceedings of the 12th ACM Conference on Recommender Systems · doi:10.1145/3240323.3240370 · Cited by 331
Metadata
- Authors
- Cremonesi, P., Koren, Y., & Turrin, R.
- Venue
- Proceedings of the fourth ACM conference on Recommender systems
- Publisher
- ACM
- Published
- 2010-09-26
- Pages
- 39-46
- OpenAlex
- W2150886314
- Cited by
- 1,410
- References
- 2
- FWCI
- 37.78
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Cremonesi, P., Koren, Y., & Turrin, R. (2010). Performance of recommender algorithms on top-n recommendation tasks. Proceedings of the fourth ACM conference on Recommender systems. https://doi.org/10.1145/1864708.1864721
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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.
