Keywords-Driven Paper Recommendation Based on Undirected Paper Citation Graph

Liu, Hanwen

doi:10.21203/rs.3.rs-144551/v2

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Moreover, these ap- VOLUME 4, 2016 proaches cannot recommend relevant research articles since they are based on user profiles and do not consider the contextual text of the articles. In [18], a keyword-driven article recommendation approach is proposed utilizing an undirected paper citation graph for recommending research articles. The keywords taken from the user query are regarded as a Steiner tree problem.…”

Section: Related Studymentioning

confidence: 99%

“…Some other recommendation approaches are query-based [8], [9] and return relevant articles based on the query from the researchers [10]. Apart from the user profile-based and user query-based approaches, there are some other approaches that includes collaborative filtering [8], [11], [12], content-based filtering [13], [14], citationbased [15]- [17], graph-based [18], and some other hybrid approaches [19], [20]. However, these approaches also incorporate the user/researcher profiles.…”

Section: Introductionmentioning

confidence: 99%

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Recommending Research Articles: A Multi-Level Chronological Learning-Based Approach Using Unsupervised Keyphrase Extraction and Lexical Similarity Calculation

et al. 2021

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A research article recommendation approach aims to recommend appropriate research articles to analogous researchers to help them better grasp a new topic in a particular research area. Due to the accessibility of research articles on the web, it is tedious to recommend a relevant article to a researcher who strives to understand a particular article. Most of the existing approaches for recommending research articles are metadata-based, citation-based, bibliographic coupling-based, content-based, and collaborative filtering-based. They require a large amount of data and do not recommend reference articles to the researcher who wants to understand a particular article going through the reference articles of that particular article. Therefore, an approach that can recommend reference articles for a given article is needed. In this paper, a new multi-level chronological learning-based approach is proposed for recommending research articles to understand the topics/concepts of an article in detail. The proposed method utilizes the TeKET keyphrase extraction technique, among other unsupervised techniques, which performs better in extracting keyphrases from the articles. Cosine and Jaccard similarity measures are employed to calculate the similarity between the parent article and its reference articles using the extracted keyphrases. The cosine similarity measure outperforms the Jaccard similarity measure for finding and recommending relevant articles to understand a particular article. The performance of the recommendation approach seems satisfactory, with an NDCG value of 0.87. The proposed approach can play an essential role alongside other existing approaches to recommend research articles.

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Section: Related Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recommending Research Articles: A Multi-Level Chronological Learning-Based Approach Using Unsupervised Keyphrase Extraction and Lexical Similarity Calculation

et al. 2021

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Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph

Wang,

Du,

Chen

2024

Applied Sciences

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This paper addresses the challenges of data sparsity and personalization limitations inherent in current recommendation systems when processing extensive academic paper datasets. To overcome these issues, the present work introduces an innovative recommendation model that integrates the wealth of structured information from knowledge graphs and refines the amalgamation of temporal and relational data. By applying attention mechanisms and neural network technologies, the model thoroughly explores the text characteristics of papers and the evolving patterns of user behaviors. Additionally, the model elevates the accuracy and personalization of recommendations by meticulously examining citation patterns among papers and the networks of author collaboration. The experimental findings show that the present model surpasses baseline models on all evaluation metrics, thereby enhancing the precision and personalization of academic paper recommendations.

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Multi-Dimensional Sport Physique Evaluation and Ranking for Healthcare Based on TOPSIS

Zhuo

Zhang

Quan

et al. 2022

Journal of Organizational and End User Computing

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Typically, through monitoring, collecting, integrating and analyzing the sport scores of individuals, we can know about the sport physique of the individuals in a real-time manner, so as to further guarantee a high-quality health level of people. However, in the abovementioned sport scores-based sport physique monitoring scenario, two major challenges are raised. First, the collected sport scores for sport physique monitoring are often multi-dimensional, which make it hard to evaluate the sport physique of individuals fairly and reasonably. Moreover, the multiple dimensions of sport scores are often of different weights or significances, which raise a challenging task to objectively evaluate the sport physique of an individual. Considering these challenges, a novel multi-dimensional sport physique evaluation and ranking method, named MSPER, is proposed in this research work for better healthcare for individuals, which is based on the TOPSIS theory. At last, a case study is presented to show the concrete procedure of MSPER method.

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Keywords-Driven Paper Recommendation Based on Undirected Paper Citation Graph

Cited by 3 publications

References 15 publications

Recommending Research Articles: A Multi-Level Chronological Learning-Based Approach Using Unsupervised Keyphrase Extraction and Lexical Similarity Calculation

Recommending Research Articles: A Multi-Level Chronological Learning-Based Approach Using Unsupervised Keyphrase Extraction and Lexical Similarity Calculation

Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph

Multi-Dimensional Sport Physique Evaluation and Ranking for Healthcare Based on TOPSIS

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