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This research aims to develop a context-aware query suggestion approach that considers prior queries as context.

August 24, 2008

Context-aware query suggestion by mining click-through and session data

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This research aims to develop a context-aware query suggestion approach that considers prior queries as context.
Proposed a context-aware query suggestion approach in two steps: offline model learning and online suggestion.
Summarized queries into concepts using clustering of click-through data.
Constructed a concept sequence suffix tree from session data for query suggestion.
The approach significantly outperformed two baseline methods in suggestion coverage and quality.
Tested on a commercial search engine with 1.8 billion search queries and 840 million sessions.

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Randomized trial demonstrates improved query suggestions in search engines, indicating enhanced user experience.

Cao et al. (2008) studied this question.

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