The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Research Paper Recommender Systems: A Random-Walk Based Approach
Abstract
Every day researchers from all over the world have to filter the huge mass of existing research papers with the crucial aim of finding out useful publications related to their current work. In this paper we propose a research paper recommending algorithm based on the citation graph and random-walker properties. The PaperRank algorithm is able to assign a preference score to a set of documents contained in a digital library and linked one each other by bibliographic references. A data set of papers extracted by ACM portal has been used for testing and very promising performances have been measured
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Cites
Haveliwala, T. H. (2002) · Proceedings of the 11th international conference on World Wide Web · doi:10.1145/511446.511513 · Cited by 1,567
Cited by
Random Walks: A Review of Algorithms and Applications
Xia, F., Liu, J., Nie, H., Fu, Y., Wan, L., & Kong, X. (2020) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2019.2952908 · Cited by 295
Xia, F., Wang, W., Bekele, T. M., & Liu, H. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2641460 · Cited by 266
A Graph-based model for context-aware recommendation using implicit feedback data
Yao, W., He, J., Huang, G., Cao, J., & Zhang, Y. (2015) · World Wide Web · doi:10.1007/s11280-014-0307-z · Cited by 54
Metadata
- Authors
- Gori, M., & Pucci, A.
- Venue
- 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06)
- Publisher
- IEEE
- Published
- 2006-12
- Pages
- 778-781
- OpenAlex
- W2102870745
- Cited by
- 172
- References
- 1
- FWCI
- 1.03
- 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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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.
