Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

3-2009

Abstract

Web query recommendation has long been considered a key feature of search engines. Building a good Web query recommendation system, however, is very difficult due to the fundamental challenge of predicting users' search intent, especially given the limited user context information. In this paper, we propose a novel "sequential query prediction" approach that tries to grasp a user's search intent based on his/her past query sequence and its resemblance to historical query sequence models mined from massive search engine logs. Different query sequence models were examined, including the naive variable length N-gram model, Variable Memory Markov (VMM) model, and our proposed Mixture Variable Memory Markov (MVMM) model. Extensive experiments were conducted to benchmark our sequence prediction algorithms against two conventional pairwise approaches on large-scale search logs extracted from a commercial search engine. Results show that the sequence-wise approaches significantly outperform the conventional pair-wise ones in terms of prediction accuracy. In particular, our MVMM approach, consistently leads the pack, making it an effective and practical approach towards Web query recommendation.

Keywords

Query recommendation, Sequential query prediction, Mixture variable memory Markov model

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

25th IEEE International Conference on Data Engineering ICDE 2009: Proceedings, 29 March-2 April, Shanghai, China

First Page

1443

Last Page

1454

ISBN

9780769535456

Identifier

10.1109/ICDE.2009.71

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Copyright Owner and License

Authors

Additional URL

https://doi.ieeecomputersociety.org/10.1109/ICDE.2009.71

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