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Collaborative Language Models for Localized Query Prediction

Collaborative Language Models for Localized Query Prediction Collaborative Language Models for Localized Query Prediction YI FANG, Santa Clara University ZIAD AL BAWAB, Microsoft JEAN-FRANCOIS CRESPO, Google Localized query prediction (LQP) is the task of estimating web query trends for a specific location. This problem subsumes many interesting personalized web applications such as personalization for buzz query detection, for query expansion, and for query recommendation. These personalized applications can greatly enhance user interaction with web search engines by providing more customized information discovered from user input (i.e., queries), but the LQP task has rarely been investigated in the literature. Although exist abundant work on estimating global web search trends does exist, it often encounters the big challenge of data sparsity when personalization comes into play. In this article, we tackle the LQP task by proposing a series of collaborative language models (CLMs). CLMs alleviate the data sparsity issue by collaboratively collecting queries and trend information from the other locations. The traditional statistical language models assume a fixed background language model, which loses the taste of personalization. In contrast, CLMs are personalized language models with flexible background language models customized to various locations. The most sophisticated CLM enables the collaboration to adapt to specific query topics, which http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Collaborative Language Models for Localized Query Prediction

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References (33)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2014 by ACM Inc.
ISSN
2160-6455
DOI
10.1145/2622617
Publisher site
See Article on Publisher Site

Abstract

Collaborative Language Models for Localized Query Prediction YI FANG, Santa Clara University ZIAD AL BAWAB, Microsoft JEAN-FRANCOIS CRESPO, Google Localized query prediction (LQP) is the task of estimating web query trends for a specific location. This problem subsumes many interesting personalized web applications such as personalization for buzz query detection, for query expansion, and for query recommendation. These personalized applications can greatly enhance user interaction with web search engines by providing more customized information discovered from user input (i.e., queries), but the LQP task has rarely been investigated in the literature. Although exist abundant work on estimating global web search trends does exist, it often encounters the big challenge of data sparsity when personalization comes into play. In this article, we tackle the LQP task by proposing a series of collaborative language models (CLMs). CLMs alleviate the data sparsity issue by collaboratively collecting queries and trend information from the other locations. The traditional statistical language models assume a fixed background language model, which loses the taste of personalization. In contrast, CLMs are personalized language models with flexible background language models customized to various locations. The most sophisticated CLM enables the collaboration to adapt to specific query topics, which

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS) – Association for Computing Machinery

Published: Jun 1, 2014

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