Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
Query suggestion based on user landing pages
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Learning latent semantic relations from clickthrough data for query suggestion
Proceedings of the 17th ACM conference on Information and knowledge management
Query suggestions using query-flow graphs
Proceedings of the 2009 workshop on Web Search Click Data
Query clustering using click-through graph
Proceedings of the 18th international conference on World wide web
Query similarity by projecting the query-flow graph
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A structured approach to query recommendation with social annotation data
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A unified framework for recommending diverse and relevant queries
Proceedings of the 20th international conference on World wide web
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Query recommendation has been widely applied in modern search engines to help users in their information seeking activities. Recently, the query-flow graph has shown its utility in query recommendation. However, there are two major problems in directly using query-flow graph for recommendation. On one hand, due to the sparsity of the graph, one may not well handle the recommendation for many dangling queries in the graph. On the other hand, without addressing the ambiguous intents in such an aggregated graph, one may generate recommendations either with multiple intents mixed together or dominated by certain intent. In this paper, we propose a novel mixture model that describes the generation of the query-flow graph. With this model, we can identify the hidden intents of queries from the graph. We then apply an intent-biased random walk over the graph for query recommendation. Empirical experiments are conducted based on real world query logs, and both the qualitative and quantitative results demonstrate the effectiveness of our approach.