Leveraging publication metadata and social data into FolkRank for scientific publication recommendation

Doerfel, Stephan; Jäschke, Robert; Hotho, Andreas; Stumme, Gerd

doi:10.1145/2365934.2365937

Cited by 16 publications

(11 citation statements)

References 14 publications

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“…There are several research paper recommendation methods which focus on finding similarity between research articles [14]. These methods include: (1) collaborative filtering [15] (2) meta-data based [16], [17] (3) content-based [18], [19] (4) citation-based [9], [10], [20], [21] (5) multi-level citation network [13] (6) and 7user profile-based [22]- [24] approaches.…”

Section: Related Workmentioning

confidence: 99%

“…Meta-data based methods [16], [17] find similarity between research papers by comparing the meta-data of research papers which includes title of the research paper, name of authors, keywords and date of publication. The main advantage of using meta-data methods is the free availability of research paper meta-data even if they are published in paid journals.…”

Section: Related Workmentioning

confidence: 99%

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A Hybrid Approach Toward Research Paper Recommendation Using Centrality Measures and Author Ranking

et al. 2019

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The volume of research articles in digital repositories is increasing. This spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries. The problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories. In the traditional recommendation approaches, the results of the query miss many high-quality papers, in the related work section, which are either published recently or have low citation count. To overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles. Many research paper recommendation approaches have been implemented which includes collaborative filtering-based, content-based, and citation analysis-based techniques. The collaborative filtering-based approaches primarily use paper-citation matrix for recommendations, whereas the content-based approaches only consider the content of the paper. The citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest. It is therefore very difficult for a recommender system to recommend high-quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information. The proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors' collaboration network finds key authors from those papers. The papers selected by this hybrid approach are then recommended to the user. The results have shown that our proposed method performs exceedingly well as compared with the state-of-the-art existing systems, such as Google scholar and multilevel simultaneous citation network.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach Toward Research Paper Recommendation Using Centrality Measures and Author Ranking

et al. 2019

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“…One domain that is actively developing and applying these methods is the area of research data management. Possible use cases include the development of recommendation systems for similar publications (e.g., Doerfel et al [6]), the visualization of research networks (e.g., Thiele et al [7]) or plagiarism detection (e.g., Potthast et al [8]).…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Recognition and Extraction of PDF Document Elements

et al. 2019

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In the age of digitalization, the collection and analysis of large amounts of data is becoming increasingly important for enterprises to improve their businesses and processes, such as the introduction of new services or the realization of resource-efficient production. Enterprises concentrate strongly on the integration, analysis and processing of their data. Unfortunately, the majority of data analysis focuses on structured and semi-structured data, although unstructured data such as text documents or images account for the largest share of all available enterprise data. One reason for this is that most of this data is not machine-readable and requires dedicated analysis methods, such as natural language processing for analyzing textual documents or object recognition for recognizing objects in images. Especially in the latter case, the analysis methods depend strongly on the application. However, there are also data formats, such as PDF documents, which are not machine-readable and consist of many different document elements such as tables, figures or text sections. Although the analysis of PDF documents is a major challenge, they are used in all enterprises and contain various information that may contribute to analysis use cases. In order to enable their efficient retrievability and analysis, it is necessary to identify the different types of document elements so that we are able to process them with tailor-made approaches. In this paper, we propose a system that forms the basis for structuring unstructured PDF documents, so that the identified document elements can subsequently be retrieved and analyzed with tailor-made approaches. Due to the high diversity of possible document elements and analysis methods, this paper focuses on the automatic identification and extraction of data visualizations, algorithms, other diagram-like objects and tables from a mixed document body. For that, we present two different approaches. The first approach uses methods from the area of deep learning and rule-based image processing whereas the second approach is purely based on deep learning. To train our neural networks, we manually annotated a large corpus of PDF documents with our own annotation tool, of which both are being published together with this paper. The results of our extraction pipeline show that we are able to automatically extract graphical items with a precision of 0.73 and a recall of 0.8. For tables, we reach a precision of 0.78 and a recall of 0.94.

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“…CF and CB approaches have unique advantages and disadvantages. Several researchers have attempted to combine both techniques and generate hybrid ones to improve performance [11,12,14,36]. The main assumption for hybrid methods is that fusing the algorithms could provide more accurate recommendations than a single algorithm could and that the disadvantages of each algorithm could be overcome by other algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…However, these approaches cannot achieve the expected performance as in taste-related domains (movies, videos and online purchases) because of the data sparsity of the preference matrix, in which the number of scholars (users) is too small and the number of articles (items) is large. Hence, current studies have focused on hybrid recommendation approaches to leverage the advantages of CB and CF approaches and to alleviate their disadvantages [10–14]. Current hybrid methods combine relevance and connectivity features to build recommendation models while ignoring information quality.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach for article recommendation in research social networks

Sun

Jiang

Cheng

et al. 2017

Journal of Information Science

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With the prevalence of research social networks, determining effective methods for recommending scientific articles to online scholars has become a challenging and complex task. Current studies on article recommendation works are focused on digital libraries and reference sharing websites while studies on research social networking websites have seldom been conducted. Existing content-based approaches or collaborative filtering approaches suffer from the problem of data sparsity. The quality information of articles has been largely ignored in previous studies, thus raising the need for a unified recommendation framework. We propose a hybrid approach to combine relevance, connectivity and quality to recommend scientific articles. The effectiveness of the proposed framework and methods is verified using a user study on a real research social network website. The results demonstrate that our proposed methods outperform baseline methods.

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Leveraging publication metadata and social data into FolkRank for scientific publication recommendation

Cited by 16 publications

References 14 publications

A Hybrid Approach Toward Research Paper Recommendation Using Centrality Measures and Author Ranking

A Hybrid Approach Toward Research Paper Recommendation Using Centrality Measures and Author Ranking

Data-Driven Recognition and Extraction of PDF Document Elements

A hybrid approach for article recommendation in research social networks

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