A New Metric for Measuring Relatedness of ScientificPapers Based on Non-Textual Features

Zarrinkalam, Fattane; Kahani, Mohsen

doi:10.4236/iim.2012.44016

Cited by 9 publications

(6 citation statements)

References 16 publications

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“…They encompassed tools to supervise inadequate information during the provision of the user's preferences, and, in that's way, they improved the acquisition of the user profiles. Some measurement techniques have been utilized by the recommender systems for research papers [101][102][103] such as index h [104], bibliographic coupling [105], and co-citation [106] approaches. Other related studies on this field comprise recommender system for educational digital library [107], academic notifying services [108], automatic accumulation of academic studies [109][110][111], expert research [112], research discoveries via recommender systems [113,114], academic events recommender system [115], venue recommendations for research papers [116], patent citation recommendations [117], and recommendations for research datasets [118].…”

Section: R E L a T E D W O R Kmentioning

confidence: 99%

Context-Aware and Sequential Pattern Mining based recommendations for Research Papers: A Hybrid Approach

Amaad

Jhamat

Riaz³

et al. 2020

jictra

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The availability of huge volumes of online research papers over scholarly communities has been increasing rapidly with the evolution of the Internet. Meanwhile, several researchers confront troubles while retrieving suitable and relevant research papers according to their research necessities due to information overload. Besides, the research necessities vary from researcher to researcher according to their contextual state and the online behavior in sequential access. Conventional recommendation approaches for instance content-based filtering (CBF) and collaborative filtering (CF) utilize content features and rankings correspondingly, in order to produce recommendations for the researchers. In spite of this, it is inevitable to incorporate scholar’s contextual information and sequential access behavior into the recommendation procedure to generate accurate and personalized recommendations for research papers. Conventional recommender systems do not incorporate such information in the recommended procedure to compute similarities of scholars and provide recommendations; thus, they are more liable to produce an irrelevant list of recommendations in a scholarly environment. Moreover, conventional recommendation approaches generate inaccurate recommendations in presence of a high level of sparsity in the rankings. In this article, we introduce a novel method for research paper recommendations that incorporates the benefits of collective filtering (CF), context-awareness, and sequential pattern mining (SPM) to propose research papers to scholars in a hybrid manner. Context-awareness in our methodology involves the scholar's contextual state, such as skill level and research goals; SPM is used to mine weblogs and reveal sequential access actions of scholars, and CF is used to measure predictions based on correlations between scholars and generate context-aware and sequential trend mining based recommendations for the targeted scholars. Experimental evaluations of our approach indicate the excellence of our approach over other baseline approaches in terms of precision, recall, F1, and mean absolute error (MAE).

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Section: R E L a T E D W O R Kmentioning

confidence: 99%

Context-Aware and Sequential Pattern Mining based recommendations for Research Papers: A Hybrid Approach

Amaad

Jhamat

Riaz³

et al. 2020

jictra

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“…Anyone can recognized our proposed approach in categorizing papers as relating to measuring papers' relatedness. Some approaches have been tried to measure similarity of two papers but by their non-textual features [22] in comparing to our proposed approach does its categorization using textual vector of each paper. However for relatedness of scientific papers, the approach proposed in [22] model a digital library on a directed graph in which every node represents one entity: a paper, an author or a venue.…”

Section: Related Workmentioning

confidence: 99%

Low-Power Themes Classifier (LPTC): A Human-Expert-Based Approach for Classification of Scientific Papers/Theses with Low-Power Theme

Abasi¹,

Ghaznavi‐Ghoushchi²

2012

IIM

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Document classification is widely applied in many scientific areas and academic environments, using NLP techniques and term extraction algorithms like CValue, TfIdf, TermEx, GlossEx, Weirdness and the others like. Nevertheless, they mainly have weaknesses in extracting most important terms when input text has not been rectified grammatically, or even has non-alphabetic methodical and math or chemical notations, and cross-domain inference of terms and phrases. In this paper, we propose a novel Text-Categorization and Term-Extraction method based on human-expert choice of classified categories. Papers are the training phase substances of the proposed algorithm. They have been already labeled with some scientific pre-defined field specific categories, by a human expert, especially one with high experiences and researches and surveys in the field. Our approach thereafter extracts (concept) terms of the labeled papers of each category and assigns all to the category. Categorization of test papers is then applied based on their extracted terms and further comparing with each category’s terms. Besides, our approach will produce semantic enabled outputs that are useful for many goals such as knowledge bases and data sets complement of the Linked Data cloud and for semantic querying of them by some languages such as SparQL. Besides, further finding classified papers’ gained topic or class will be easy by using URIs contained in the ontological outputs. The experimental results, comparing LPTC with five well-known term extraction algorithms by measuring precision and recall, show that categorization effectiveness can be achieved using our approach. In other words, the method LPTC is significantly superior to CValue, TfIdf, TermEx, GlossEx and Weirdness in the target study. As well, we conclude that higher number of papers for training, even higher precision we have

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“…Örneğin, kelime tabanlı yaklaşımlar, farklı alanlardaki özdeş kavramların değişik kullanımlarının neden olduğu karışıklıktan etkilenmektedir (bazen "yapay öğrenme" ile "makine öğrenmesi" eş anlamlı olarak kullanılmaktadır). Öte yandan, iki farklı kavram farklı alanlarda aynı adla anılabilir (Zarrinkalam ve Kahani, 2012). Bu durum ilgili yayınların göz ardı edilmesine ya da listede ilgisiz yayınların yer almasına yol açabilir (Küçüktunç ve diğerleri, 2015, s. 2).…”

Section: Introductionunclassified

Incremental Refinement of Relevance Rankings: Introducing a New Method Supported with Pennant Retrieval

Akbulut

Tonta

2022

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Purpose: Relevance ranking algorithms rank retrieved documents based on the degrees of topical similarity (relevance) between search queries and documents. This paper aims to introduce a new relevance ranking method combining a probabilistic topic modeling algorithm with the “pennant retrieval” method using citation data. Data and Method: We applied this method to the iSearch corpus consisting of c. 435,000 physics papers. We first ran the topic modeling algorithm on titles and summaries of all papers for 65 search queries and obtained the relevance ranking lists. We then used the pennant retrieval to fuse the citation data with the existing relevance rankings, thereby incrementally refining the results. The outcome produced better relevance rankings with papers covering various aspects of the topic searched as well as the more marginal ones. The Maximal Marginal Relevance (MMR) algorithm was used to evaluate the retrieval performance of the proposed method by finding out its effect on relevance ranking algorithms that we used. Findings: Findings suggest that the terms used in different contexts in the papers might sometimes be overlooked by the topic modeling algorithm. Yet, the fusion of citation data to relevance ranking lists provides additional contextual information, thereby further enriching the results with diverse (interdisciplinary) papers of higher relevance. Moreover, results can easily be re-ranked and personalized. Implications: We argue that once it is tested on dynamic corpora for computational load, robustness, replicability, and scalability, the proposed method can in time be used in both local and international information systems such as TR-Dizin, Web of Science, and Scopus. Originality: The proposed method is, as far as we know, the first one that shows that relevance rankings produced with a topic modeling algorithm can be incrementally refined using pennant retrieval techniques based on citation data.

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A New Metric for Measuring Relatedness of ScientificPapers Based on Non-Textual Features

Cited by 9 publications

References 16 publications

Context-Aware and Sequential Pattern Mining based recommendations for Research Papers: A Hybrid Approach

Context-Aware and Sequential Pattern Mining based recommendations for Research Papers: A Hybrid Approach

Low-Power Themes Classifier (LPTC): A Human-Expert-Based Approach for Classification of Scientific Papers/Theses with Low-Power Theme

Incremental Refinement of Relevance Rankings: Introducing a New Method Supported with Pennant Retrieval

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