Dynamic Query Intent Prediction from a Search Log Stream

Dynamic Query Intent Prediction from a Search Log Stream

Wael K. Hanna (Mansoura University, Mansoura, Egypt), Aziza Saad Asem (Mansoura Unversity, Mansoura, Egypt) and M. B. Senousy (Sadat Academy for Management Sciences, Cairo, Egypt)
Copyright: © 2016 |Pages: 20
DOI: 10.4018/IJIRR.2016040104
OnDemand PDF Download:
No Current Special Offers


The users that used search engines are obligated to express their goals in few words (queries). Sometimes search queries are ambiguous. Moreover, the users' intents are dynamically evolving. This paper analyzes the user's query logs to classify the related queries, the related intent topic categories and the related intent types and use this classification to dynamically predict the users' future queries, its intent topic and its intent type. AOL Search Query Log is taken as an experimental data set. Then use evaluation metrics to evaluate the prediction results.
Article Preview

Problem Definition

This problem is defined formally as follows. Let s = (a1, a2, a3… an) be a query log stream of a user (search stream of a single user as a document) [Hsin et al. 2011]. [Di et al.2014]. But the user query intent change dynamically, so let’s divide the query log steam to stream bins (b1, b2… bn). Query log stream bins are interactions between user and the search engine in specific times or depend on number of queries like in our experiment for simplicity.

Each stream bin contains several actions where each action ai (1 <= i <= n) is either a query submitted by the user or a URL clicked by the user The actions a1, a2, …, an are ordered by the time of their occurrences, with a1 having the earliest occurrence time [Hsin et al., 2011].

Stream bins in query stream can be divided into different pairs (H1, F1), (H2, F2)… (Hn-1, Fn-1) where Hj and Fj are two action sequences [Hsin, et al., 2011]. For each stream bin, it is possible to view Hj as a history of the actions that a user has performed during time tn-1 to tn, and Fj as the future action that the user will perform during the time tn to tn+1. The goal is to dynamically predict Fj through the stream while given that Hj is known.

Complete Article List

Search this Journal:
Open Access Articles: Forthcoming
Volume 12: 4 Issues (2022): Forthcoming, Available for Pre-Order
Volume 11: 4 Issues (2021): 3 Released, 1 Forthcoming
Volume 10: 4 Issues (2020)
Volume 9: 4 Issues (2019)
Volume 8: 4 Issues (2018)
Volume 7: 4 Issues (2017)
Volume 6: 4 Issues (2016)
Volume 5: 4 Issues (2015)
Volume 4: 4 Issues (2014)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing