Document Embeddings vs. Keyphrases vs. Terms for Recommender Systems: A Large-Scale Online Evaluation

Collins, Andrew; Beel, Joeran

doi:10.1109/jcdl.2019.00027

Cited by 13 publications

(18 citation statements)

References 10 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%

“…We have to refrain from only utilising the labels the works give themselves (see Table 2 for an overview of selflabels of works which do classify themselves). Works do not necessarily (clearly) state, which category they belong to [28,49,60]. Another problem with self-labelling is authors' individual definitions of categories while disregarding all possible ones (as e.g.…”

Section: Meta Analysismentioning

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%

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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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Section: Problem Statementmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Meta Analysismentioning

confidence: 99%

Section: Introductionmentioning

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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“…Another way of assessing the effectiveness of these models would be to explore their impact on other tasks as an extrinsic evaluation. To the best of our knowledge, there is no previously published research on that matter despite many downstream tasks that already benefit from keyphrase information such as article recommendation [12] or browsing interfaces [20] in digital libraries. This points to an interesting future direction that allows for a deeper understanding of the limitations of current models.…”

Section: F@10 Map Modelmentioning

confidence: 99%

Large-Scale Evaluation of Keyphrase Extraction Models

Gallina

Boudin

Daille

2020

Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020

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Keyphrase extraction models are usually evaluated under different, not directly comparable, experimental setups. As a result, it remains unclear how well proposed models actually perform, and how they compare to each other. In this work, we address this issue by presenting a systematic large-scale analysis of state-ofthe-art keyphrase extraction models involving multiple benchmark datasets from various sources and domains. Our main results reveal that state-of-the-art models are in fact still challenged by simple baselines on some datasets. We also present new insights about the impact of using author-or reader-assigned keyphrases as a proxy for gold standard, and give recommendations for strong baselines and reliable benchmark datasets.

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