A Novel Paper Recommendation Method Empowered by Knowledge Graph: for Research Beginners

Wang, Bangchao; Weng, Ziyang; Wang, Yanping

doi:10.48550/arxiv.2103.08819

Cited by 1 publication

(14 citation statements)

References 21 publications

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“…As input we can either specify an initial paper [28], keywords [117], a user [37], a user and a paper [5] or more complex information such as user-constructed knowledge graphs [109]. Users can be modelled as a combination of features of papers they interacted with [19,21], e.g.…”

Section: Problem Statementmentioning

confidence: 99%

“…The intended goal of authors of papers could, e.g. either be to recommend papers which should be read [109] by a user or recommend papers which are simply somehow related to an initial paper [28], by topic, citations or user interactions.…”

Section: Problem Statementmentioning

confidence: 99%

“…Among the most common methods was their application on papers: in an unspecified representation [30,119], Word2Vec [19,37,44,45,55,104,113], Word2Vec of LDA top words [24,107], Doc2vec [21,28,48,62,63,107], Doc2Vec of word pairs [109], BERT [123] and SBERT [5,19]. Most times these approaches do not mention which part of the paper to use as input but some specifically mention the following parts: titles [37], titles and abstracts [28,45], titles, abstracts and bodies [48], keywords and paper [119].…”

Section: Embeddingmentioning

confidence: 99%

“…The rapidly increasing number of publications leads to a large quantity of possibly relevant papers [6] for more specific tasks such as finding related papers [28], finding ones to read [109] or literature search in general to inspire new directions and understand the state-of-the-art approaches [46]. Overall researchers typically spend a large amount of time on searching for relevant related work [7].…”

Section: Introductionmentioning

confidence: 99%

“…Overall researchers typically spend a large amount of time on searching for relevant related work [7]. Keyword-based search options are insufficient to find relevant papers [9,52,109], they require some form of initial knowledge about a field. Oftentimes, users' information needs are not explicitly specified [56] which impedes this task further.…”

Section: Introductionmentioning

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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