Causal Analysis of User Search Query Intent

Gahangir Hossain,James Haarbauer,Brian King,Jonathan Abdo

doi:10.4236/jcc.2016.414009

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.

Highlights

Bayesian networks modeled with cause and effects with each variable represented by a node, and causal relationships by an arrow, are known as Causal Bayesian Networks (CBNs) [1]
We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction
We looked at the application of CBNs, compared with Naïve Bays and Bays Net classifiers on very large datasets

Summary

Introduction

Bayesian networks modeled with cause and effects with each variable represented by a node, and causal relationships by an arrow (an edge), are known as Causal Bayesian Networks (CBNs) [1]. The direction of the arrow indicates the direction of causality and researchers represent it with directed acyclic graphs (DAGs) with causal interpretation on Bayesian network (BN). Causal reasoning and causal understanding are the causal interpretation part of a CBN, while a CBN is used for human intentional action recognition. Pereira [2] explores the usage of CBN for intention prediction in two different scenarios. The first is to Aesop’s fable of the crow and the fox in which the crow attempts to predict the intent of the fox and to choose an appropriate action in response. The second is the primary focus of the paper and uses CBN to predict the in-

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Conclusion