Paper Info
Title
Context-aware query suggestion by mining click-through and session data
Abstract
Query suggestion plays an important role in improving the usability of search engines. Although some recently proposed methods can make meaningful query suggestions by mining query patterns from search logs, none of them are context-aware - they do not take into account the immediately preceding queries as context in query suggestion. In this paper, we propose a novel context-aware query suggestion approach which is in two steps. In the offine model-learning step, to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. Then, from session data a concept sequence suffix tree is constructed as the query suggestion model. In the online query suggestion step, 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 sufix tree, our approach suggests queries to the user in a context-aware manner. We test our approach on a large-scale search log of a commercial search engine containing 1:8 billion search queries, 2:6 billion clicks, and 840 million query sessions. The experimental results clearly show that our approach outperforms two baseline methods in both coverage and quality of suggestions.
Year
DOI
Venue
2008
10.1145/1401890.1401995
KDD
Keywords
Field
DocType
billion search query,mining query pattern,context-aware query suggestion,online query suggestion step,session data,million query session,meaningful query suggestion,query sequence,mining click-through,novel context-aware query suggestion,query suggestion model,query suggestion,commercial search engine,search engine
Query optimization,Data mining,Web search query,RDF query language,Query language,Information retrieval,Query expansion,Computer science,Sargable,Web query classification,Spatial query
Conference
Citations 
PageRank 
References 
259
7.57
22
Authors
7
Search Limit
100259
Name
Order
Citations
PageRank
Huanhuan Cao195733.09
Daxin Jiang2131672.60
Jian Pei319002995.54
Qi He42326132.92
Zhen Liao540513.33
Enhong Chen62106165.57
Hang Li76294317.05