Explore semantic topics and author communities for citation recommendation in bipartite bibliographic network

Tao Dai, Li Zhu, Xiaoyan Cai, Shirui Pan, Sheng Yuan

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Citation recommendation is the task of suggesting a list of references for an author given a manuscript. This is important for academic research for it provides an efficient and easy way to find relevant literatures. In this paper, we propose a novel probabilistic topic model to automatically recommend citations for researchers. The model considers not only text content similarity between papers but also community relevance among authors for effective citation recommendation. To fully utilize content and diversified link information in a bibliographic network, we extend LDA with matrix factorization, so that semantic topic learning and community detection are essentially reinforcing each other during parameter estimation. We also develop a flexible way to generate a family of citation link probability functions, which can substantially increase the model capacity. Experimental results on the ANN and DBLP dataset show that our model outperforms baseline algorithms for citation recommendation, and is capable of generating qualified author communities and topics.

Original languageEnglish
Pages (from-to)957-975
Number of pages19
JournalJournal of Ambient Intelligence and Humanized Computing
Volume9
Issue number4
DOIs
Publication statusPublished - Aug 2018
Externally publishedYes

Keywords

  • Citation recommendation
  • Community detection
  • Nonnegative matrix factorization
  • Topic model

Cite this

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title = "Explore semantic topics and author communities for citation recommendation in bipartite bibliographic network",
abstract = "Citation recommendation is the task of suggesting a list of references for an author given a manuscript. This is important for academic research for it provides an efficient and easy way to find relevant literatures. In this paper, we propose a novel probabilistic topic model to automatically recommend citations for researchers. The model considers not only text content similarity between papers but also community relevance among authors for effective citation recommendation. To fully utilize content and diversified link information in a bibliographic network, we extend LDA with matrix factorization, so that semantic topic learning and community detection are essentially reinforcing each other during parameter estimation. We also develop a flexible way to generate a family of citation link probability functions, which can substantially increase the model capacity. Experimental results on the ANN and DBLP dataset show that our model outperforms baseline algorithms for citation recommendation, and is capable of generating qualified author communities and topics.",
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Explore semantic topics and author communities for citation recommendation in bipartite bibliographic network. / Dai, Tao; Zhu, Li; Cai, Xiaoyan; Pan, Shirui; Yuan, Sheng.

In: Journal of Ambient Intelligence and Humanized Computing, Vol. 9, No. 4, 08.2018, p. 957-975.

Research output: Contribution to journalArticleResearchpeer-review

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AU - Dai, Tao

AU - Zhu, Li

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AU - Pan, Shirui

AU - Yuan, Sheng

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AB - Citation recommendation is the task of suggesting a list of references for an author given a manuscript. This is important for academic research for it provides an efficient and easy way to find relevant literatures. In this paper, we propose a novel probabilistic topic model to automatically recommend citations for researchers. The model considers not only text content similarity between papers but also community relevance among authors for effective citation recommendation. To fully utilize content and diversified link information in a bibliographic network, we extend LDA with matrix factorization, so that semantic topic learning and community detection are essentially reinforcing each other during parameter estimation. We also develop a flexible way to generate a family of citation link probability functions, which can substantially increase the model capacity. Experimental results on the ANN and DBLP dataset show that our model outperforms baseline algorithms for citation recommendation, and is capable of generating qualified author communities and topics.

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