The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Collaborative topic modeling for recommending scientific articles
Abstract
Researchers have access to large online archives of scientific articles. As a consequence, finding relevant papers has become more difficult. Newly formed online communities of researchers sharing citations provides a new way to solve this problem. In this paper, we develop an algorithm to recommend scientific articles to users of an online community. Our approach combines the merits of traditional collaborative filtering and probabilistic topic modeling. It provides an interpretable latent structure for users and items, and can form recommendations about both existing and newly published articles. We study a large subset of data from CiteULike, a bibliography sharing service, and show that our algorithm provides a more effective recommender system than traditional collaborative filtering.
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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
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Artificial intelligence: a modern approach
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Bertsekas, D. P. (1997) · Journal of the Operational Research Society · doi:10.1038/sj.jors.2600425 · Cited by 11,726
Matrix Factorization Techniques for Recommender Systems
Koren, Y., Bell, R., & Volinsky, C. (2009) · Computer · doi:10.1109/mc.2009.263 · Cited by 11,673
Recognition-by-components: A theory of human image understanding.
Biederman, I. (1987) · Psychological Review · doi:10.1037/0033-295x.94.2.115 · Cited by 5,621
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · CHANCE · doi:10.1080/09332480.2005.10722754 · Cited by 4,702
Probabilistic Matrix Factorization
Mnih, A., & Salakhutdinov, R. (2007) · neural information processing systems · Cited by 3,798
Neural Information Processing Systems
(2018) · The Deep Learning Revolution · doi:10.7551/mitpress/11474.003.0014 · Cited by 3,730
Collaborative Filtering for Implicit Feedback Datasets
Hu, Y., Koren, Y., & Volinsky, C. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.22 · Cited by 3,170
Cited by
Collaborative Deep Learning for Recommender Systems
Wang, H., Wang, N., & Yeung, D.-Y. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783273 · Cited by 1,657
Inferring Networks of Substitutable and Complementary Products
McAuley, J., Pandey, R., & Leskovec, J. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783381 · Cited by 645
Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda
Mustak, M., Salminen, J., Plé, L., & Wirtz, J. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.10.044 · Cited by 555
When large language models meet personalization: perspectives of challenges and opportunities
Chen, J., Liu, Z., Huang, X., Wu, C., Liu, Q., Jiang, G., Pu, Y., Lei, Y., Chen, X., Wang, X., et al. (2024) · World Wide Web · doi:10.1007/s11280-024-01276-1 · Cited by 367
“information, such as user profiles, item-side information [13], [14], such as item brands and item categories, and interactionbased information [15], such as reviews and comments.”
Predicting academic performance: a systematic literature review
Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. N. (2018) · Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education · doi:10.1145/3293881.3295783 · Cited by 355
Metadata
- Authors
- Wang, C., & Blei, D. M.
- Venue
- Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2011-08-21
- Pages
- 448-456
- OpenAlex
- W2135790056
- Cited by
- 1,527
- References
- 32
- FWCI
- 40.62
- 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: Collaborative topic modeling for recommending scientific articles. https://app.cochat.ai/papers/10.1145/2020408.2020480
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- Wang, C., & Blei, D. M. (2011). Collaborative topic modeling for recommending scientific articles. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/2020408.2020480
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.
