We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavi...We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavista, and Alltheweb). These logs are parsed and sorted in order to create a data structure that was used to build a CBN. This network is used to predict the next term or terms that the user may be about to search (type). We looked at the application of CBNs, compared with Naive Bays and Bays Net classifiers on very large datasets. To simulate our proposed results, we took a small sample of search data logs to predict intentional query typing. Additionally, problems that arise with the use of such a data structure are addressed individually along with the solutions used and their prediction accuracy and sensitivity.展开更多
Existing research has shown that political crisis events can directly impact the tourism industry.However,the current methods suffer from potential changes of unobserved variables,which poses challenges for a reliable...Existing research has shown that political crisis events can directly impact the tourism industry.However,the current methods suffer from potential changes of unobserved variables,which poses challenges for a reliable evaluation of the political crisis impacts.This paper proposes a panel counterfactual approach with Internet search index,which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest.It also provides a tool to examine potential channels through which the crisis may affect tourist outflows.This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea.Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place.This paper provides essential evidence to help decision-makers improve the management of the tourism crisis.展开更多
文摘We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavista, and Alltheweb). These logs are parsed and sorted in order to create a data structure that was used to build a CBN. This network is used to predict the next term or terms that the user may be about to search (type). We looked at the application of CBNs, compared with Naive Bays and Bays Net classifiers on very large datasets. To simulate our proposed results, we took a small sample of search data logs to predict intentional query typing. Additionally, problems that arise with the use of such a data structure are addressed individually along with the solutions used and their prediction accuracy and sensitivity.
基金supported by the National Natural Science Foundation of China under Grant No.72203246(HUANG Bai's work)the National Natural Science Foundation of China under Grant Nos.72322016,72073126,71988101,71973116 and 72091212Young Elite Scientists Sponsorship Program by CAST (SUN Yuying's work)。
文摘Existing research has shown that political crisis events can directly impact the tourism industry.However,the current methods suffer from potential changes of unobserved variables,which poses challenges for a reliable evaluation of the political crisis impacts.This paper proposes a panel counterfactual approach with Internet search index,which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest.It also provides a tool to examine potential channels through which the crisis may affect tourist outflows.This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea.Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place.This paper provides essential evidence to help decision-makers improve the management of the tourism crisis.
