Novelty Paper Recommendation Using Citation Authority Diffusion

Chen, Chun‐Han; Mayanglambam, Sushilata Devi; Hsu, Fu-Yuan; Lu, Cheng-Yu; Lee, Hahn-Ming; Ho, Jan-Ming

doi:10.1109/taai.2011.29

Cited by 13 publications

(7 citation statements)

References 16 publications

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“…One of these is the dataset in which the publications are located and the other is the dataset in which authors and author relations (such as institutional information) are found. As in Chen and colleagues [12], cited value has also played an important role in this study. It is stated that TF-IDF is used in many methods proposed in this area.…”

Section: Related Workmentioning

confidence: 62%

“…One of the major problems encountered in implementing the PageRank algorithm on the academic area is that publications generally decline in value as they go past, while the number of citation increases in the past. In another study, a recommendation system based on references of academic publications is available in [12]. The emphasis of this work, unlike the number of citations, is to score the citations by looking at the information, such as how many different sources and how many different academics have come from, in terms of the number of main cited citations.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

User Profile Based Paper Recommendation System

Kaya

2018

ijisae

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With the spread of science and the increasing number of researchers working in academic fields, there has also been a significant increase in the number of academic publications. Researchers always follow new works published for keeping their knowledge up to date. However, due to thousands of academic publications published every day from many academic sources, academics are not always able to find publications about their subjects. Today, almost all of online academic databases employ a recommendation module that only considers the studies similar to the paper that the user looked at. However, a recommendation system based on the information of a single article is often not enough. In this study, the proposed method recommends by considering user's publications, user's co-authors and co-authors' papers. Therefore, metadata of the articles published by the researcher in the past are evaluated as time-awareness by the method we proposed. In this way, the most relevant articles to the user's profile can be found by using the proposed method in the data repository created from the exact contents of hundreds of thousands of academic works. The method uses TF-IDF frequency-based similarity analysis method. In the evaluation phase, the performance of the proposed method was examined. The accuracy of the method was measured by several different tests. The results are very promising and demonstrate that the method can produce accurate and quality results.

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Section: Related Workmentioning

confidence: 62%

Section: Related Workmentioning

confidence: 99%

User Profile Based Paper Recommendation System

Kaya

2018

ijisae

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“…To help junior researchers read more classic papers online, the two principles (download persistence and citation approaching) are proposed to determine whether a paper is a classic paper, which will be recommended to the junior researchers [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%

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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“…Novelty of a research paper towards the particular research area is the focus of a study where the citation network is leveraged to identify critical papers (Chen et al, 2011). A methodology called as Citation Authority Diffusion (CAD) is proposed to identify important papers from Google Scholar for particular research topics.…”

Section: Related Workmentioning

confidence: 99%