Paper recommendation using citation proximity in bibliographic coupling

Habib, Raja; Afzal, Muhammad

doi:10.3906/elk-1608-180

Cited by 21 publications

(15 citation statements)

References 21 publications

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“…Apart from the paper recommendation methods mentioned before, researchers invent some other paper recommendation techniques such as modified latent factor model [30], hash map [94], bibliographic coupling [95], etc. In this section, some novel paper recommendation techniques will be introduced.…”

Section: E Othersmentioning

confidence: 99%

“…A Citation Authority Diffusion (CAD) methodology is proposed to identify the key papers [100]. Techniques like Multi-Criteria Decision Aiding [101], [102], Bibliographic Coupling [95], Belief Propagation (BP) [91], [103], Deep learning [24], Canonical Correlations Analysis (CCA) [104], Singular Value Decomposition (SVD) [105] appear in some researches to recommend papers.…”

Section: E Othersmentioning

confidence: 99%

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Scientific Paper Recommendation: A Survey

et al. 2019

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Globally, the recommendation services have become important due to the fact that they support e-commerce applications and different research communities. Recommender systems have a large number of applications in many fields, including economic, education, and scientific research. Different empirical studies have shown that the recommender systems are more effective and reliable than the keyword-based search engines for extracting useful knowledge from massive amounts of data. The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation. Scientific paper recommendation aims to recommend new articles or classical articles that match researchers' interests. It has become an attractive area of study since the number of scholarly papers increases exponentially. In this paper, we first introduce the importance and advantages of the paper recommender systems. Second, we review the recommendation algorithms and methods, such as Content-based, collaborative filtering, graph-based, and hybrid methods. Then, we introduce the evaluation methods of different recommender systems. Finally, we summarize the open issues in the paper recommender systems, including cold start, sparsity, scalability, privacy, serendipity, and unified scholarly data standards. The purpose of this survey is to provide comprehensive reviews on the scholarly paper recommendation.

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Section: E Othersmentioning

confidence: 99%

Section: E Othersmentioning

confidence: 99%

Scientific Paper Recommendation: A Survey

et al. 2019

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“…Set's detail Professors (6) Set: 7, 17 PhD students (9) Set: 11, 13, 16 PhD students (2) + MS student (1) Set: 12 MS students (33) Set: 1, 2, 3, 4, 5, 6,8,9,10,12,14,15 Each of the three members in specific groups was given a list (table) mentioning a source document (target document) and some co-cited documents (candidate papers). This list consisted of abstracts, author information, and downloadable links of all candidate documents as well as the target document.…”

Section: Usersmentioning

confidence: 99%

“…Afterwards, the similarity between generated summaries is calculated to determine relevance among them [10]. One study assigned publications to prespecified ontology-based contexts and computed context similarities between them [9]. Another performed term similarity between documents by extracting terms from title, abstract, and body components of a research paper [15].…”

Section: Related Workmentioning

confidence: 99%

“…All of these deficiencies in the existing works have motivated us to overcome them so that most widely known systems can be improved. In the literature, various approaches such as citation-based approaches [2,[5][6][7][8], content/text-based approaches [9,10], collaborative filtering-based approaches [11,12], and metadata-based approaches [13,14] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

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Extending co-citation using sections of research articles

Khan¹,

Shahid²,

Afzal³

2018

Turk J Elec Eng & Comp Sci

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The excessive amount of digital information has made it crucial to extract the relevant information. This hinders researchers in finding documents pertaining to their research. There exist various state-of-the-art techniques, such as co-citation, bibliographic coupling, and their recent extensions like citation proximity analysis and citation order analysis, that recommend the relevant documents against the posed query. Most of these approaches are statistical in nature and thus can further be extended by incorporating some semantics to enhance the results. In this paper, we present an extension of a co-citation-based technique to identify the most relevant documents to co-cited document(s).The proposed system explores in-text citation frequencies and in-text citation patterns of co-cited documents within the different logical sections of cited-by papers. Furthermore, we have evaluated the proposed approach with the co-citation approach and citation proximity analysis (CPA) approach on a dataset acquired from CiteSeer. The outcomes revealed that most of the time the proposed approach outperformed other state-of-the-art techniques. The average correlation of the proposed approach is increased by 68% as compared to the co-citation-based approach. In comparison with CPA approach, the average correlation of the proposed approach is increased by 39% with respect to gold-standard rankings.

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Seed‐based information retrieval in networks of research publications: Evaluation of direct citations, bibliographic coupling, co‐citations, and PubMed‐related article score

Sjögårde,

Ahlgren

2024

Asso for Info Science & Tech

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In this contribution, we deal with seed‐based information retrieval in networks of research publications. Using systematic reviews as a baseline, and publication data from the NIH Open Citation Collection, we compare the performance of the three citation‐based approaches direct citation, co‐citation, and bibliographic coupling with respect to recall and precision measures. In addition, we include the PubMed‐related article score as well as combined approaches in the comparison. We also provide a fairly comprehensive review of earlier research in which citation relations have been used for information retrieval purposes. The results show an advantage for co‐citation over bibliographic coupling and direct citation. However, combining the three approaches outperforms the exclusive use of co‐citation in the study. The results further indicate, in line with previous research, that combining citation‐based approaches with textual approaches enhances the performance of seed‐based information retrieval. The results from the study may guide approaches combining citation‐based and textual approaches in their choice of citation similarity measures. We suggest that future research use more structured approaches to evaluate methods for seed‐based retrieval of publications, including comparative approaches as well as the elaboration of common data sets and baselines for evaluation.

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Paper recommendation using citation proximity in bibliographic coupling

Cited by 21 publications

References 21 publications

Scientific Paper Recommendation: A Survey

Scientific Paper Recommendation: A Survey

Extending co-citation using sections of research articles

Seed‐based information retrieval in networks of research publications: Evaluation of direct citations, bibliographic coupling, co‐citations, and PubMed‐related article score

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