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Mining concept sequences from large-scale search logs for context-aware query suggestion

Publication ,  Journal Article
Liao, Z; Jiang, D; Chen, E; Pei, J; Cao, H; Li, H
Published in: ACM Transactions on Intelligent Systems and Technology
October 1, 2011

Query suggestion plays an important role in improving usability of search engines. Although some recently proposed methods provide query suggestions by mining query patterns from search logs, none of them models the immediately preceding queries as context systematically, and uses context information effectively in query suggestions. Context-aware query suggestion is challenging in both modeling context and scaling up query suggestion using context. In this article, we propose a novel context-aware query suggestion approach. To tackle the challenges, our approach consists of two stages. In the first, offline model-learning stage, to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. A concept sequence suffix tree is then constructed from session data as a context-aware query suggestionmodel. In the second, online query suggestion stage, a user's search context is captured by mapping the query sequence submitted by the user to a sequence of concepts. By looking up the context in the concept sequence suffix tree, we suggest to the user context-aware queries. We test our approach on large-scale search logs of a commercial search engine containing 4.0 billion Web queries, 5.9 billion clicks, and 1.87 billion search sessions. The experimental results clearly show that our approach outperforms three baseline methods in both coverage and quality of suggestions.© 2011 ACM.

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Published In

ACM Transactions on Intelligent Systems and Technology

DOI

EISSN

2157-6912

ISSN

2157-6904

Publication Date

October 1, 2011

Volume

3

Issue

1

Related Subject Headings

  • 4611 Machine learning
  • 4602 Artificial intelligence
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing
 

Citation

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MLA
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Liao, Z., Jiang, D., Chen, E., Pei, J., Cao, H., & Li, H. (2011). Mining concept sequences from large-scale search logs for context-aware query suggestion. ACM Transactions on Intelligent Systems and Technology, 3(1). https://doi.org/10.1145/2036264.2036281
Liao, Z., D. Jiang, E. Chen, J. Pei, H. Cao, and H. Li. “Mining concept sequences from large-scale search logs for context-aware query suggestion.” ACM Transactions on Intelligent Systems and Technology 3, no. 1 (October 1, 2011). https://doi.org/10.1145/2036264.2036281.
Liao Z, Jiang D, Chen E, Pei J, Cao H, Li H. Mining concept sequences from large-scale search logs for context-aware query suggestion. ACM Transactions on Intelligent Systems and Technology. 2011 Oct 1;3(1).
Liao, Z., et al. “Mining concept sequences from large-scale search logs for context-aware query suggestion.” ACM Transactions on Intelligent Systems and Technology, vol. 3, no. 1, Oct. 2011. Scopus, doi:10.1145/2036264.2036281.
Liao Z, Jiang D, Chen E, Pei J, Cao H, Li H. Mining concept sequences from large-scale search logs for context-aware query suggestion. ACM Transactions on Intelligent Systems and Technology. 2011 Oct 1;3(1).

Published In

ACM Transactions on Intelligent Systems and Technology

DOI

EISSN

2157-6912

ISSN

2157-6904

Publication Date

October 1, 2011

Volume

3

Issue

1

Related Subject Headings

  • 4611 Machine learning
  • 4602 Artificial intelligence
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing