User-controlled hybrid recommendation for academic papers

Rahdari, Behnam; Brusilovsky, Peter

doi:10.1145/3308557.3308717

Cited by 5 publications

(15 citation statements)

References 2 publications

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“…[69] Hybrid [71] Content-based [74][75][76] Content-based [84] Hybrid [86] Content-based [89] Collaborative filtering [88] Hybrid [93,94] In-text citation frequencies-based ×…”

Section: Meta Analysismentioning

confidence: 99%

“…We differentiate between profiles derived from user interactions and ones derived from papers. Most user profiles are constructed from users' actual interactions: unspecified historical interaction [30,37,56,57,64,118], the mean of the representation of interacted with papers [19], time decayed interaction behaviour [62], liked papers [69,123], bookmarked papers [84,119], read papers [111,113], rated papers [3,4,110], clicked on papers [24,26,49], categories of clicked papers [1], features of clicked papers [104], tweets [74][75][76], social interactions [65] and explicitly defined topics of interest tags [119].…”

Section: User Profilementioning

confidence: 99%

“…For paper-based popularity measures we encountered some citation-based definitions such as vitality [117], citation [22] and theirs centrality [96] in the citation network. Additionally, some approaches incorporated less formal interactions: number of downloads [56], social media mentions [119] and normalised number of bookmarks [84].…”

Section: Popularitymentioning

confidence: 99%

“…Cosine similarity was used between preferences or profiles of users and papers in the following ways: unspecified representations [63,84,113,115], Boolean representation of users and keywords [60], tf-idf vectors [21,[74][75][76], cf-idf vectors [74][75][76] and hcf-idf vectors [74][75][76].…”

Section: Cosine Similaritymentioning

confidence: 99%

“…Rahdari and Brusilovsky [84] observe paper recommendation for participants of scientific conferences. Users' profiles are composed of their past publications.…”

Section: Paper Recommendation Systemsmentioning

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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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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“…[69] Hybrid [71] Content-based [74][75][76] Content-based [84] Hybrid [86] Content-based [89] Collaborative filtering [88] Hybrid [93,94] In-text citation frequencies-based ×…”

Section: Meta Analysismentioning

confidence: 99%

Section: User Profilementioning

confidence: 99%

Section: Popularitymentioning

confidence: 99%

Section: Cosine Similaritymentioning

confidence: 99%

“…Rahdari and Brusilovsky [84] observe paper recommendation for participants of scientific conferences. Users' profiles are composed of their past publications.…”

Section: Paper Recommendation Systemsmentioning

confidence: 99%

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