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
The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interest...The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.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.
文摘The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.
