The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
Evaluating Recommender Systems
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Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
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
A NEW MEASURE OF RANK CORRELATION
KENDALL, M. G. (1938) · Biometrika · doi:10.1093/biomet/30.1-2.81 · Cited by 7,269
Addelman, S. (1979) · Technometrics · doi:10.1080/00401706.1979.10489788 · Cited by 6,750
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
Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Breese, J. S., Heckerman, D. E., & Kadie, C. M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.7363 · Cited by 4,772
Item-based top-N recommendation algorithms
Deshpande, M., & Karypis, G. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963776 · Cited by 2,164
Improving recommendation lists through topic diversification
Ziegler, C.-N., McNee, S. M., Konstan, J. A., & Lausen, G. (2005) · Proceedings of the 14th international conference on World Wide Web - WWW '05 · doi:10.1145/1060745.1060754 · Cited by 1,892
Analysis of recommendation algorithms for e-commerce
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000) · Proceedings of the 2nd ACM conference on Electronic commerce · doi:10.1145/352871.352887 · Cited by 1,827
Cited by
Hybrid recommender systems: A systematic literature review
Çano, E., & Morisio, M. (2017) · Intelligent Data Analysis · doi:10.3233/ida-163209 · Cited by 339
Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems
Kunkel, J., Donkers, T., Michael, L., Barbu, C.-M., & Ziegler, J. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300717 · Cited by 156
Progress in context-aware recommender systems — An overview
Raza, S., & Ding, C. (2019) · Computer Science Review · doi:10.1016/j.cosrev.2019.01.001 · Cited by 151
Metadata
- Authors
- Gunawardana, A., & Shani, G.
- Venue
- Recommender Systems Handbook
- Publisher
- Springer US
- Published
- 2015
- Pages
- 265-308
- OpenAlex
- W298464722
- Cited by
- 283
- References
- 73
- FWCI
- 81.43
- Type
- book chapter
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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- Gunawardana, A., & Shani, G. (2015). Evaluating Recommender Systems. Recommender Systems Handbook. https://doi.org/10.1007/978-1-4899-7637-6_8
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
