[Retracted] A Multi-RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency-Based Scientific Influence Modeling

Table 3

RMSE and precision of different models of different fields.

(a) Precision of different models of CL field
	p@10	p@20	p@30	p@40	p@50	p@60	p@70	p@80
ARIMA	0.6000	0.5000	0.5333	0.5000	0.4600	0.4667	0.4857	0.4750
GRU	0.7000	0.8000	0.8000	0.7250	0.7200	0.6500	0.6571	0.6750
LSTM	0.7000	0.8000	0.7667	0.7750	0.7200	0.7167	0.7000	0.7250
ENDE	0.6000	0.6000	0.6000	0.5500	0.5800	0.5667	0.5286	0.5500
TARNN	0.6000	0.6000	0.6000	0.5500	0.5600	0.5500	0.5286	0.5375
DARNN	0.6000	0.6000	0.6000	0.5500	0.5800	0.5667	0.5286	0.5375
CONI	0.7000	0.8000	0.7667	0.7250	0.6800	0.6333	0.6286	0.6750
SASC	0.7000	0.9000	0.9000	0.8500	0.8000	0.8167	0.8143	0.8000
(b) Precision of different models of CV field
ARIMA	0.8000	0.6000	0.6000	0.6250	0.5400	0.5500	0.5714	0.6250
GRU	0.9000	0.6000	0.6667	0.6250	0.5600	0.5667	0.6000	0.6375
LSTM	0.9000	0.6000	0.6667	0.6250	0.5800	0.5833	0.6000	0.6375
ENDE	0.9000	0.6000	0.6667	0.6250	0.5800	0.5667	0.6000	0.6375
TARNN	0.9000	0.6000	0.6667	0.6250	0.5600	0.5833	0.6000	0.6250
DARNN	0.9000	0.6000	0.6667	0.6250	0.5800	0.5667	0.5857	0.6500
CONI	0.9000	0.6500	0.7000	0.6750	0.6800	0.7000	0.6857	0.7000
SASC	1.0000	0.9000	0.8333	0.8750	0.8400	0.8333	0.8714	0.8500
(c) Precision of different models of ML field
ARIMA	0.6000	0.5500	0.5333	0.6000	0.5800	0.5500	0.5286	0.6125
GRU	0.7000	0.6000	0.6000	0.7000	0.6200	0.5500	0.6143	0.6250
LSTM	0.8000	0.8500	0.7667	0.7750	0.7400	0.7500	0.7714	0.7875
ENDE	0.7000	0.6000	0.6000	0.7000	0.6200	0.5667	0.6143	0.6250
TARNN	0.7000	0.6000	0.6000	0.7000	0.6200	0.5833	0.6143	0.6375
DARNN	0.7000	0.6000	0.6000	0.7000	0.6200	0.5833	0.6143	0.6250
CONI	0.7000	0.6000	0.6000	0.7000	0.6200	0.5833	0.6143	0.6250
SASC	1.0000	0.9000	0.9000	0.9000	0.9000	0.9000	0.8714	0.9250
(d) Precision of different models of IR field
ARIMA	0.7000	0.5500	0.6333	0.7000	0.7000	0.7000	0.7000	0.7250
GRU	0.8000	0.6000	0.7000	0.8000	0.7600	0.8167	0.8000	0.7875
LSTM	0.8000	0.6000	0.6333	0.7250	0.7600	0.7500	0.7571	0.7625
ENDE	0.8000	0.5500	0.6667	0.7000	0.7600	0.7000	0.7429	0.7625
TARNN	0.8000	0.6000	0.6667	0.7250	0.7600	0.7167	0.7429	0.7500
DARNN	0.8000	0.5500	0.6333	0.7250	0.7600	0.7167	0.7429	0.7500
CONI	0.8000	0.5500	0.6333	0.7250	0.7600	0.7167	0.7625	0.7556
SASC	0.8000	0.7500	0.8000	0.8000	0.8000	0.8333	0.8143	0.8000
(e) Precision of different models of AI field
ARIMA	0.5000	0.5500	0.5000	0.5750	0.6000	0.6667	0.6857	0.6375
GRU	0.7000	0.6500	0.5000	0.5750	0.6200	0.6833	0.7000	0.6750
LSTM	0.6000	0.7000	0.5000	0.5750	0.6200	0.6667	0.7000	0.6625
ENDE	0.6000	0.6500	0.5333	0.5750	0.6200	0.6500	0.7000	0.6500
TARNN	0.6000	0.6500	0.5333	0.5750	0.6200	0.6500	0.6714	0.6750
DARNN	0.6000	0.6500	0.5000	0.5750	0.6200	0.6833	0.7000	0.6500
CONI	0.6000	0.6500	0.5000	0.6000	0.6400	0.6667	0.7000	0.6500
SASC	0.9000	0.8500	0.8333	0.9000	0.9000	0.8667	0.8429	0.8250
(f) Average precision of different models of five fields
ARIMA	0.6400	0.5500	0.5600	0.6000	0.5760	0.5867	0.5943	0.6150
GRU	0.7600	0.6500	0.6533	0.6850	0.6560	0.6533	0.6743	0.6800
LSTM	0.7600	0.7100	0.6667	0.6950	0.6840	0.6933	0.7057	0.7150
ENDE	0.7200	0.6000	0.6133	0.6300	0.6320	0.6100	0.6371	0.6450
TARNN	0.7200	0.6100	0.6133	0.6350	0.6240	0.6167	0.6314	0.6450
DARNN	0.7200	0.6000	0.6000	0.6350	0.6320	0.6233	0.6343	0.6425
CONI	0.7400	0.6500	0.6400	0.6850	0.6760	0.6600	0.6714	0.6825
SASC	0.8800	0.8600	0.8533	0.8650	0.8480	0.8500	0.8429	0.8400