A Paper Recommendation System Based on User's Research Interests

Bulut, Betül; Kaya, Buket; Alhajj, Reda; Kaya, Mehmet

doi:10.1109/asonam.2018.8508313

Cited by 16 publications

(6 citation statements)

References 25 publications

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“…In recent years, the exponential increase in the number of journals/conferences and spreading of interdisciplinary studies make this choice more difficult. To facilitate this process, some web sites or services have offered [30], such as ConferencesAlerts [31],WikiCFP [32], CallForPapers [21] and TheCfpList [33]. Moreover, while 'ConfSearch' enables scanning by looking at author and conference relations [34],'ConfAssist' provides information about whether or not conferences are at the top level [35].…”

Section: A Calculation Of Similarities Between Candidate Articles And...mentioning

confidence: 99%

Author-Profile-Based Journal Recommendation for a Candidate Article: Using Hybrid Semantic Similarity and Trend Analysis

Yasar¹,

Kaya²

2023

IEEE Access

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Finding the right journal for a manuscript to be submitted is difficult and often time-consuming because authors take into account some criteria while searching for the appropriate journal for their manuscript. One of the most important criteria is the content similarity of the journals and manuscript. For this purpose, the subject of the manuscript should be in accordance with the scope of the journal. Also, the manuscript content should be closed to the journals' trend for higher chance of acceptance. Second criterion is to take into account the impact-factor, acceptance-rate, review-time and publishing houses of the journal, which are suitable for the author's past publication profile. In this study, a novel method is proposed in which both the content of the article and the author / authors profile are considered together to find the appropriate journal. To the best of our knowledge, this is the first effort in this direction. Experimental results conducted on real data sets have shown that the proposed method is applicable and performs high accuracy values.

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Section: A Calculation Of Similarities Between Candidate Articles And...mentioning

confidence: 99%

Author-Profile-Based Journal Recommendation for a Candidate Article: Using Hybrid Semantic Similarity and Trend Analysis

Yasar¹,

Kaya²

2023

IEEE Access

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“…In our work, we focus on content-based scientific publication recommendation approaches that implicitly derive user interest models from their authored papers. Only few works exist that follow this approach [2,[89][90][91][92][93][94][95][96][97]. These works have built user models with keyphrases, concepts, or topics extracted from the researcher's past publications using a bag-of-word (BoW) model, TF-IDF, topic modeling, keyphrase extraction, or embedding techniques.…”

Section: User Modelingmentioning

confidence: 99%

“…Nishioka et al [91,92,93] constructed user models from research papers and tweets based on different variants of TF-IDF. To generate user models, Bulut et al [94,95] considered a user's past publications and represented users as the sum of the features of their publications. All the required metadata, such as the title, year, author, abstract, and keyword of each publication, were extracted and merged together in a profile represented by TF-IDF.…”

Section: User Modelingmentioning

confidence: 99%

“…For application between papers, similarity is computed between the feature vectors of the input paper on the one hand and the set of the candidate papers to recommend on the other hand [11,24,[72][73][74][75][98][99][100][101]. For application between users and papers, similarity is computed using the constructed user profile and the feature vectors of the set of the candidate papers to recommend [2,9,25,80,[82][83][84][87][88][89][90][91][92][93][94][95]. Most of the similarity computation is based on papers and users represented by TF-IDF [72,80,83,84,87,88,[90][91][92][93][94][95][99][100][101].…”

Section: Recommendation Generationmentioning

confidence: 99%

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Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence Encoders

Guesmi,

Chatti,

Kadhim

et al. 2023

MTI

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The fast growth of data in the academic field has contributed to making recommendation systems for scientific papers more popular. Content-based filtering (CBF), a pivotal technique in recommender systems (RS), holds particular significance in the realm of scientific publication recommendations. In a content-based scientific publication RS, recommendations are composed by observing the features of users and papers. Content-based recommendation encompasses three primary steps, namely, item representation, user modeling, and recommendation generation. A crucial part of generating recommendations is the user modeling process. Nevertheless, this step is often neglected in existing content-based scientific publication RS. Moreover, most existing approaches do not capture the semantics of user models and papers. To address these limitations, in this paper we present a transparent Recommendation and Interest Modeling Application (RIMA), a content-based scientific publication RS that implicitly derives user interest models from their authored papers. To address the semantic issues, RIMA combines word embedding-based keyphrase extraction techniques with knowledge bases to generate semantically-enriched user interest models, and additionally leverages pretrained transformer sentence encoders to represent user models and papers and compute their similarities. The effectiveness of our approach was assessed through an offline evaluation by conducting extensive experiments on various datasets along with user study (N = 22), demonstrating that (a) combining SIFRank and SqueezeBERT as an embedding-based keyphrase extraction method with DBpedia as a knowledge base improved the quality of the user interest modeling step, and (b) using the msmarco-distilbert-base-tas-b sentence transformer model achieved better results in the recommendation generation step.

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“…A recommendation system consists of a set of tools and techniques to provide useful suggestions to the users for decision-making (Raza and Ding, 2022). In the scholarly domain, numerous recommendation systems have been developed with the focus to find relevant research articles (Bulut et al ., 2018, 2020; El Alaoui et al ., 2021; Zhu et al ., 2021), scientific collaborators (Alshareef et al ., 2018; Huang et al ., 2021; Makarov et al ., 2018; Rodrigues et al ., 2018; Wu et al ., 2020), citations (Färber et al ., 2018; Guilarte et al ., 2019; Jeong et al ., 2020; Wang et al ., 2020; Xie et al ., 2021) and journals/conferences (Ghosal et al ., 2019; Jain et al ., 2018; Wang et al ., 2018). In addition, recommending relevant Q&A is an emerging research domain.…”

Section: Introductionmentioning

confidence: 99%

Contextual analysis of scholarly communications to identify the source of disinformation on digital scholarly platforms

Desai

Mehta

Rana

2023

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PurposeScholarly communications, particularly, questions and answers (Q&A) present on digital scholarly platforms provide a new avenue to gain knowledge. However, several studies have raised a concern about the content anomalies in these Q&A and suggested a proper validation before utilizing them in scholarly applications such as influence analysis and content-based recommendation systems. The content anomalies are referred as disinformation in this research. The purpose of this research is firstly, to assess scholarly communications in order to identify disinformation and secondly, to help scholarly platforms determine the scholars who probably disseminate such disinformation. These scholars are referred as the probable sources of disinformation.Design/methodology/approachTo identify disinformation, the proposed model deduces (1) content redundancy and contextual redundancy in questions (2) contextual nonrelevance in answers with respect to the questions and (3) quality of answers with respect to the expertise of the answering scholars. Then, the model determines the probable sources of disinformation using the statistical analysis.FindingsThe model is evaluated on ResearchGate (RG) data. Results suggest that the model efficiently identifies disinformation from scholarly communications and accurately detects the probable sources of disinformation.Practical implicationsDifferent platforms with communication portals can use this model as a regulatory mechanism to restrict the prorogation of disinformation. Scholarly platforms can use this model to generate an accurate influence assessment mechanism and also relevant recommendations for their scholars.Originality/valueThe existing studies majorly deal with validating the answers using statistical measures. The proposed model focuses on questions as well as answers and performs a contextual analysis using an advanced word embedding technique.

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A Paper Recommendation System Based on User's Research Interests

Cited by 16 publications

References 25 publications

Author-Profile-Based Journal Recommendation for a Candidate Article: Using Hybrid Semantic Similarity and Trend Analysis

Author-Profile-Based Journal Recommendation for a Candidate Article: Using Hybrid Semantic Similarity and Trend Analysis

Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence Encoders

Contextual analysis of scholarly communications to identify the source of disinformation on digital scholarly platforms

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