Academic Article Recommendation by Considering the Research Field Trajectory

Lin, Shijie; Lee, Guanling; Peng, Sheng‐Lung

doi:10.1007/978-3-030-65407-8_39

Cited by 2 publications

(6 citation statements)

References 12 publications

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“…For author-based popularity measures we found unspecified ones [65] such as authority [116] as well as ones regarding the citations an author received: citation count of papers [22,96,108,119], change in citation count [25,26], annual citation count [26], number of citations related to papers [59], h-index [26]. We found two definitions of author's popularity using the graph structure of scholarly networks, namely the number of co-authors [41] and a person's centrality [108].…”

Section: Popularitymentioning

confidence: 99%

“…For application between papers we encountered the possibility of using unspecified embeddings: unspecified word or vector representations of papers [30,48,107,110], papers' key terms or top words [2,98] and key phrases [46]. We found some approaches using vector space model variants: unspecified [59], tf vectors [39,88], tf-idf vectors [42,95,111], dimensionality reduced tf-idf vectors [86] and lastly, tf-idf and entity embeddings [56]. Some approaches incorporated more advanced embedding techniques: SBERT embeddings [5], Doc2Vec embeddings [28], Doc2Vec embeddings with incorporation of their emotional score [109] and NPLM representations [29].…”

Section: Cosine Similaritymentioning

confidence: 99%

“…Lin et al [59] utilise authors' citations and years they have been publishing papers in their recommendation approach. All candidate publications are matched against user-entered keywords, the two factors of authors of these candidate publications are combined to identify the overall top recommendations.…”

Section: Paper Recommendation Systemsmentioning

confidence: 99%

“…Out of the observed systems, twelve 25 approaches [1,28,30,49,59,64,69,71,[74][75][76]107,115,116] (22.22%) only report one single measure, all others report at least two different ones. Percentages are rounded to two decimal places…”

Section: Evaluation Measuresmentioning

confidence: 99%

“…Systems using deep learning could overcome this issue [58]. There are approaches specifically targeting this problem [59,96], some [59] also introduced specific evaluation measures (totNP_EU and avgNP_EU) to quantify systems' ability to overcome the cold start problem.…”

Section: Cold Startmentioning

confidence: 99%

See 4 more Smart Citations

Scientific paper recommendation systems: a literature review of recent publications

Kreutz

Schenkel

2022

Int J Digit Libr

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Scientific writing builds upon already published papers. Manual identification of publications to read, cite or consider as related papers relies on a researcher’s ability to identify fitting keywords or initial papers from which a literature search can be started. The rapidly increasing amount of papers has called for automatic measures to find the desired relevant publications, so-called paper recommendation systems. As the number of publications increases so does the amount of paper recommendation systems. Former literature reviews focused on discussing the general landscape of approaches throughout the years and highlight the main directions. We refrain from this perspective, instead we only consider a comparatively small time frame but analyse it fully. In this literature review we discuss used methods, datasets, evaluations and open challenges encountered in all works first released between January 2019 and October 2021. The goal of this survey is to provide a comprehensive and complete overview of current paper recommendation systems.

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Section: Popularitymentioning

confidence: 99%

Section: Cosine Similaritymentioning

confidence: 99%

Section: Paper Recommendation Systemsmentioning

confidence: 99%

Section: Evaluation Measuresmentioning

confidence: 99%

Section: Cold Startmentioning

confidence: 99%

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Scientific paper recommendation systems: a literature review of recent publications

Kreutz

Schenkel

2022

Int J Digit Libr

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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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Academic Article Recommendation by Considering the Research Field Trajectory

Cited by 2 publications

References 12 publications

Scientific paper recommendation systems: a literature review of recent publications

Scientific paper recommendation systems: a literature review of recent publications

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

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