The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A content-based recommender system for computer science publications
Open accessAbstract
As computer science and information technology are making broad and deep impacts on our daily lives, more and more papers are being submitted to computer science journals and conferences. To help authors decide where they should submit their manuscripts, we present the Content-based Journals & Conferences Recommender System on computer science, as well as its web service at http://www.keaml.cn/prs/ . This system recommends suitable journals or conferences with a priority order based on the abstract of a manuscript. To follow the fast development of computer science and technology, a web crawler is employed to continuously update the training set and the learning model. To achieve interactive online response, we propose an efficient hybrid model based on chi-square feature selection and softmax regression. Our test results show that, the system can achieve an accuracy of 61.37% and suggest the best journals or conferences in about 5 s on average.
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Citations
Cites
An algorithm for suffix stripping
Porter, M. F. (1980) · Program · doi:10.1108/eb046814 · Cited by 8,096
Cited by
Automated scholarly paper review: Concepts, technologies, and challenges
Lin, J., Song, J., Zhou, Z., Chen, Y., & Shi, X. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101830 · Cited by 37
Recommendation Systems on E-Learning and Social Learning: A Systematic Review
Souabi, S., Retbi, A., Khalidi Idrissi, M. K. I., & Bennani, S. (2021) · Electronic Journal of e-Learning · doi:10.34190/ejel.19.5.2482 · Cited by 27
Metadata
- Authors
- Wang, D., Liang, Y., Xu, D., Feng, X., & Guan, R.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 157
- Pages
- 1-9
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2803478834
- Cited by
- 374
- References
- 1
- FWCI
- 51.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: A content-based recommender system for computer science publications. https://app.cochat.ai/papers/10.1016/j.knosys.2018.05.001
- The paper
- Wang, D., Liang, Y., Xu, D., Feng, X., & Guan, R. (2018). A content-based recommender system for computer science publications. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2018.05.001
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.
