Using citation-context to reduce topic drifting on pure citation-based recommendation

Khadka, Anita; Knoth, Petr

doi:10.1145/3240323.3240379

Cited by 12 publications

(12 citation statements)

References 23 publications

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“…Anika et al [27] investigated the effects of incorporating the textual information in closed proximity of the citation on the performance of the recommender system. The authors introduced a new Context Citation Dataset (C2D), which contains the citation information along with the citation context.…”

Section: Related Workmentioning

confidence: 99%

Citation Intent Classification Using Word Embedding

et al. 2021

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Citation analysis is an active area of research for various reasons. So far, statistical approaches are mainly used for citation analysis, which does not look into the internal context of the citations. Deep analysis of citation may reveal interesting findings by utilizing deep neural network algorithms. The existing scholarly datasets are best suited for statistical approaches but lack citation context, intent, and section information. Furthermore, the datasets are too small to be used with deep learning approaches. For citation intent analysis, the datasets must have a citation context labeled with different citation intent classes. Most of the datasets either do not have labeled context sentences, or the sample is too small to be generalized. In this study, we critically investigated the available datasets for citation intent and proposed an automated citation intent technique to label the citation context with citation intent. Furthermore, we annotated ten million citation contexts with citation intent from Citation Context Dataset (C2D) dataset with the help of our proposed method. We applied Global Vectors (GloVe), Infersent, and Bidirectional Encoder Representations from Transformers (BERT) word embedding methods and compared their Precision, Recall, and F1 measures. It was found that BERT embedding performs significantly better, having an 89% Precision score. The labeled dataset, which is freely available for research purposes, will enhance the study of citation context analysis. Finally, It can be used as a benchmark dataset for finding the citation motivation and function from in-text citations.

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Section: Related Workmentioning

confidence: 99%

Citation Intent Classification Using Word Embedding

et al. 2021

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“…Regarding the citation knowledge awareness, existing approaches that capture and exploit citation knowledge have focused on recommending relevant papers for a target paper [7], [18], or a manuscript [10]. Fewer works, in contrast, have focused on recommending relevant papers for a user [5], [13].…”

Section: Related Workmentioning

confidence: 99%

“…The proposed approach, hyb, is based on the item-based nearest neighbour CF heuristic 7 where content features are used to compute item similarities. In item-based CF [34], similarities between items are used to estimate scores for a (user, item) pair.…”

Section: B Hybrid Recommender Systemsmentioning

confidence: 99%

“…Regarding the recommendation of academic papers, several tasks have been addressed in the literature, including recommending relevant papers for varied targets such as a user [4], [5], a paper [6], [7], a particular snapshot of content (title, abstract, etc.) [8], a particular collection of papers [9], and a manuscript (i.e., a paper yet to be published) [10], [11].…”

Section: Introductionmentioning

confidence: 99%

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Capturing and Exploiting Citation Knowledge for Recommending Recently Published Papers

Khadka

Cantador

Fernández

2020

2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)

Self Cite

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With the continuous growth of scientific literature, discovering relevant academic papers for a researcher has become a challenging task, especially when looking for the latest, most recent papers. In this case, traditional collaborative filtering systems are ineffective, since they are unable to recommend items not previously seen, rated or cited. In this paper, we explore the potential of exploiting citation knowledge to provide a given user with relevant suggestions about recent scientific publications. A novel hybrid recommendation method that encapsulates such citation knowledge is proposed. Experimental results show improvements over baseline methods, evidencing benefits of using citation knowledge to recommend recently published papers in a personalised way. Moreover, as a result of our work, we also provide a unique dataset that, differently to previous corpora, contains detailed paper citation information.

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“…Identifying the research topics that best describe the scope of a scientific publication is a crucial task for editors, in particular because the quality of these annotations determines how effectively users are able to discover the right content in online libraries. A high-quality representation of research publications has also an effect on the performance of approaches to discovering and querying scientific articles [1], producing smart analytics [2], detecting research communities [3], extracting research entities [4], recommending publications [5], forecasting research topics [6], and so on.…”

Section: Introductionmentioning

confidence: 99%

Improving Editorial Workflow and Metadata Quality at Springer Nature

Salatino

Osborne

Birukou

et al. 2019

Lecture Notes in Computer Science

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Identifying the research topics that best describe the scope of a scientific publication is a crucial task for editors, in particular because the quality of these annotations determine how effectively users are able to discover the right content in online libraries. For this reason, Springer Nature, the world's largest academic book publisher, has traditionally entrusted this task to their most expert editors. These editors manually analyse all new books, possibly including hundreds of chapters, and produce a list of the most relevant topics. Hence, this process has traditionally been very expensive, time-consuming, and confined to a few senior editors. For these reasons, back in 2016 we developed Smart Topic Miner (STM), an ontology-driven application that assists the Springer Nature editorial team in annotating the volumes of all books covering conference proceedings in Computer Science. Since then STM has been regularly used by editors in Germany, China, Brazil, India, and Japan, for a total of about 800 volumes per year. Over the past three years the initial prototype has iteratively evolved in response to feedback from the users and evolving requirements. In this paper we present the most recent version of the tool and describe the evolution of the system over the years, the key lessons learnt, and the impact on the Springer Nature workflow. In particular, our solution has drastically reduced the time needed to annotate proceedings and significantly improved their discoverability, resulting in 9.3 million additional downloads. We also present a user study involving 9 editors, which yielded excellent results in term of usability, and report an evaluation of the new topic classifier used by STM, which outperforms previous versions in recall and F-measure.

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Using citation-context to reduce topic drifting on pure citation-based recommendation

Cited by 12 publications

References 23 publications

Citation Intent Classification Using Word Embedding

Citation Intent Classification Using Word Embedding

Capturing and Exploiting Citation Knowledge for Recommending Recently Published Papers

Improving Editorial Workflow and Metadata Quality at Springer Nature

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