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Cited by 172

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

Gori, Marco, Pucci, Augusto·2006·2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06)

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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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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CoChat. (2026). Citation check: Research Paper Recommender Systems: A Random-Walk Based Approach. https://app.cochat.ai/papers/10.1109/wi.2006.149
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Gori, M., & Pucci, A. (2006). Research Paper Recommender Systems: A Random-Walk Based Approach. 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06). https://doi.org/10.1109/wi.2006.149

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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.