The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Content-based Recommender Systems: State of the Art and Trends
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Citations
Cites
A spreading-activation theory of semantic processing.
Collins, A. M., & Loftus, E. F. (1975) · Psychological Review · doi:10.1037/0033-295x.82.6.407 · Cited by 8,141
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382
Amazon.com recommendations: item-to-item collaborative filtering
Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing · doi:10.1109/mic.2003.1167344 · Cited by 5,403
Content-Based Recommendation Systems
Pazzani, M. J., & Billsus, D. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72079-9_10 · Cited by 2,600
Internet encyclopaedias go head to head
Giles, J. (2005) · Nature · doi:10.1038/438900a · Cited by 1,978
Cited by
Facing the cold start problem in recommender systems
Lika, B., Kolomvatsos, K., & Hadjiefthymiades, S. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.09.005 · Cited by 658
Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning
Tarus, J. K., Niu, Z., & Mustafa, G. (2018) · Artificial Intelligence Review · doi:10.1007/s10462-017-9539-5 · Cited by 490
Making sense of recommendations
Yeomans, M., Shah, A., Mullainathan, S., & Kleinberg, J. (2019) · Journal of Behavioral Decision Making · doi:10.1002/bdm.2118 · Cited by 459
Recommender systems based on user reviews: the state of the art
Chen, L., Chen, G., & Wang, F. (2015) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-015-9155-5 · Cited by 403
Conversational Recommender System
Sun, Y., & Zhang, Y. (2018) · The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval · doi:10.1145/3209978.3210002 · Cited by 378
Metadata
- Authors
- Lops, P., de Gemmis, M., & Semeraro, G.
- Venue
- Recommender Systems Handbook
- Publisher
- Springer US
- Published
- 2010-10-05
- Pages
- 73-105
- OpenAlex
- W2116206254
- Cited by
- 1,898
- References
- 5
- FWCI
- 107.69
- 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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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Content-based Recommender Systems: State of the Art and Trends. https://app.cochat.ai/papers/10.1007/978-0-387-85820-3_3
- The paper
- Lops, P., de Gemmis, M., & Semeraro, G. (2011). Content-based Recommender Systems: State of the Art and Trends. Recommender Systems Handbook. https://doi.org/10.1007/978-0-387-85820-3_3
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.
