Content Based Scientific Article Recommendation System Using Deep Learning Technique

Nair, Akhil M.; Benny, Oshin; George, Jossy P.

doi:10.1007/978-981-16-1395-1_70

Cited by 14 publications

(15 citation statements)

References 14 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%

“…Of the observed paper recommendation systems, six were general systems or methods which were only applied on the domain of paper recommendation [3,4,24,60,118,121]. Two were targeting explicit set-based recommendation of publications where only all papers in the set together satisfy users' information needs [60,61], two recommend multiple papers [42,71] (e.g. on a path [42]), all the other approaches focused on recommendation of k single papers.…”

Section: Current Categorisationmentioning

confidence: 99%

“…Several approaches embed the information in their own model embedding: a heterogeneous information network [5], a two-layer NN [37], a scientific social reference network [41], the TransE model [56], node embeddings [63], paper, author and venue embedding [116], user and item embedding [118], a GRU and association rule mining model [71], a GCN embedding of users [104] and an LSTM model [113].…”

Section: Embeddingmentioning

confidence: 99%

“…We found a multitude of machine learning methods being used, from multi armed bandits [1], LSTM [24,37,113], multi-layer perceptrons [62,96,104], (bi-)GRU [37,69,71,123], matrix factorisation [4,62,69,110,111], gradient ascent or descent [41,57,63,116], some form of simple neural network [30,37,56], some form of graph neural network [19,49,104], autoencoder [4], neural collaborative filtering [62], learning methods [30,123] to DTW [48]. Three approaches ranked the papers to recommend [56,57,118] with, e.g.…”

Section: Advanced Machine Learningmentioning

confidence: 99%

“…Nair et al [71] propose C-SAR, a paper recommendation approach using a neural network. They input GloVe embeddings of paper titles into their Gated Recurrent Union model to compute probabilities of similarities of papers.…”

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%