TITLE:
Causal Analysis of User Search Query Intent
AUTHORS:
Gahangir Hossain, James Haarbauer, Jonathan Abdo, Brian King
KEYWORDS:
Causal Bayesian Networks (CBNs), Query Search, Intervention, Reasoning, Inference Mechanisms, Prediction Methods
JOURNAL NAME:
Journal of Computer and Communications,
Vol.4 No.14,
November
28,
2016
ABSTRACT: 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.