The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
On the recommending of citations for research papers
Abstract
Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings matrix. Specifically, we tested the ability of collaborative filtering to recommend citations that would be suitable additional references for a target research paper. We investigated six algorithms for selecting citations, evaluating them through offline experiments against a database of over 186,000 research papers contained in ResearchIndex. We also performed an online experiment with over 120 users to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks. We found large differences in the accuracy of the algorithms in the offline experiment, especially when balanced for coverage. In the online experiment, users felt they received quality recommendations, and were enthusiastic about the idea of receiving recommendations in this domain.
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Citations
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
Self-citationFriedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742
Cited by
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
Self-citationA user-centric evaluation framework for recommender systems
Pu, P., Chen, L., & Hu, R. (2011) · Proceedings of the fifth ACM conference on Recommender systems · doi:10.1145/2043932.2043962 · Cited by 850
Xia, F., Wang, W., Bekele, T. M., & Liu, H. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2641460 · Cited by 266
SuggestBot: using intelligent task routing to help people find work in wikipedia
Cosley, D., Frankowski, D., Terveen, L., & Riedl, J. (2007) · Proceedings of the 12th international conference on Intelligent user interfaces · doi:10.1145/1216295.1216309 · Cited by 245
Self-citationUnderstanding choice overload in recommender systems
Bollen, D., Knijnenburg, B. P., Willemsen, M. C., & Graus, M. (2010) · Proceedings of the fourth ACM conference on Recommender systems · doi:10.1145/1864708.1864724 · Cited by 232
Metadata
- Authors
- McNee, S. M., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S. K., Rashid, A. M., Konstan, J. A., & Riedl, J.
- Venue
- Proceedings of the 2002 ACM conference on Computer supported cooperative work
- Publisher
- ACM
- Published
- 2002-11-16
- Pages
- 116-125
- OpenAlex
- W2116655493
- Cited by
- 400
- References
- 2
- FWCI
- 7.65
- Type
- conference paper
- 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.
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- CoChat. (2026). Citation check: On the recommending of citations for research papers. https://app.cochat.ai/papers/10.1145/587078.587096
- The paper
- McNee, S. M., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S. K., Rashid, A. M., Konstan, J. A., & Riedl, J. (2002). On the recommending of citations for research papers. Proceedings of the 2002 ACM conference on Computer supported cooperative work. https://doi.org/10.1145/587078.587096
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.
