The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
과학연구의 급속한 발전과 함께 과학논문, 특허 등 출판물도 급속히 늘어났다. 이렇게 많은 양의 출판물에서 높은 품질과 큰 영향력을 지닌 출판물을 식별하는 것이 점점 더 중요해지고 있습니다. 인용 횟수는 출판물의 향후 영향을 나타내는 잘 알려진 지표 중 하나입니다. 그러나 출판물의 다수의 불확실한 요소를 관련 특징으로 해석하고 이를 활용하여 시간 경과에 따른 출판물의 영향을 포착하는 방법은 여전히 어려운 문제입니다. 본 논문에서는 신경 기반 인용 예측 모델을 통해 다양한 요소를 효과적으로 활용하는 접근 방식을 제시합니다. 구체적으로 제안된 모델은 연속시간 장단기 기억(cLSTM)을 적용한 NHP(Neural Hawkes Process)를 기반으로 하며, 이는 출판 공변량 및 인용을 통해 노화 효과와 잠자는 숲속의 미녀 현상을 보다 효과적으로 포착할 수 있다. 중요합니다. 두 데이터 세트에 대한 실험 결과는 제안된 접근 방식이 최첨단 기준선보다 성능이 우수하다는 것을 보여줍니다. 또한 성능 향상에 대한 공변량의 기여도 검증되었습니다.
Lisha LIU
Hangzhou Dianzi University,University of Yamanashi
Dongjin YU
Hangzhou Dianzi University
Dongjing WANG
Hangzhou Dianzi University
Fumiyo FUKUMOTO
University of Yamanashi
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부
Lisha LIU, Dongjin YU, Dongjing WANG, Fumiyo FUKUMOTO, "Citation Count Prediction Based on Neural Hawkes Model" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 11, pp. 2379-2388, November 2020, doi: 10.1587/transinf.2020EDP7051.
Abstract: With the rapid development of scientific research, the number of publications, such as scientific papers and patents, has grown rapidly. It becomes increasingly important to identify those with high quality and great impact from such a large volume of publications. Citation count is one of the well-known indicators of the future impact of the publications. However, how to interpret a large number of uncertain factors of publications as relevant features and utilize them to capture the impact of publications over time is still a challenging problem. This paper presents an approach that effectively leverages a variety of factors with a neural-based citation prediction model. Specifically, the proposed model is based on the Neural Hawkes Process (NHP) with the continuous-time Long Short-Term Memory (cLSTM), which can capture the aging effect and the phenomenon of sleeping beauty more effectively from publication covariates as well as citation counts. The experimental results on two datasets show that the proposed approach outperforms the state-of-the-art baselines. In addition, the contribution of covariates to performance improvement is also verified.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7051/_p
부
@ARTICLE{e103-d_11_2379,
author={Lisha LIU, Dongjin YU, Dongjing WANG, Fumiyo FUKUMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Citation Count Prediction Based on Neural Hawkes Model},
year={2020},
volume={E103-D},
number={11},
pages={2379-2388},
abstract={With the rapid development of scientific research, the number of publications, such as scientific papers and patents, has grown rapidly. It becomes increasingly important to identify those with high quality and great impact from such a large volume of publications. Citation count is one of the well-known indicators of the future impact of the publications. However, how to interpret a large number of uncertain factors of publications as relevant features and utilize them to capture the impact of publications over time is still a challenging problem. This paper presents an approach that effectively leverages a variety of factors with a neural-based citation prediction model. Specifically, the proposed model is based on the Neural Hawkes Process (NHP) with the continuous-time Long Short-Term Memory (cLSTM), which can capture the aging effect and the phenomenon of sleeping beauty more effectively from publication covariates as well as citation counts. The experimental results on two datasets show that the proposed approach outperforms the state-of-the-art baselines. In addition, the contribution of covariates to performance improvement is also verified.},
keywords={},
doi={10.1587/transinf.2020EDP7051},
ISSN={1745-1361},
month={November},}
부
TY - JOUR
TI - Citation Count Prediction Based on Neural Hawkes Model
T2 - IEICE TRANSACTIONS on Information
SP - 2379
EP - 2388
AU - Lisha LIU
AU - Dongjin YU
AU - Dongjing WANG
AU - Fumiyo FUKUMOTO
PY - 2020
DO - 10.1587/transinf.2020EDP7051
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2020
AB - With the rapid development of scientific research, the number of publications, such as scientific papers and patents, has grown rapidly. It becomes increasingly important to identify those with high quality and great impact from such a large volume of publications. Citation count is one of the well-known indicators of the future impact of the publications. However, how to interpret a large number of uncertain factors of publications as relevant features and utilize them to capture the impact of publications over time is still a challenging problem. This paper presents an approach that effectively leverages a variety of factors with a neural-based citation prediction model. Specifically, the proposed model is based on the Neural Hawkes Process (NHP) with the continuous-time Long Short-Term Memory (cLSTM), which can capture the aging effect and the phenomenon of sleeping beauty more effectively from publication covariates as well as citation counts. The experimental results on two datasets show that the proposed approach outperforms the state-of-the-art baselines. In addition, the contribution of covariates to performance improvement is also verified.
ER -