Clustering user queries of a search engine

By Ji-rong Wen, Jian-Yun Nie, HongJiang Zhang. The Web Conference, 2001

525 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/27154320a85ad803fe7e9ba1521807fb6ea5b4b6

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In Signal Processing

Citations in the corpus

Click-through-based cross-view learning for image search (2014, methodology): “combined query content information and clickthrough information and applied a density-based method to cluster queries [28].”
Query Recommendation Using Query Logs in Search Engines (2004, background): “[6] propose to cluster similar queries to recommen d URLs to frequently asked queries of a search engine.”
Methods and tools for ontology evolution (2004, background): “The recent analyses show that web users typically submit very short queries to search engines and the average length of web queries is less than two “terms” [151].”
Learning query intent from regularized click graphs (2008, methodology): “A click graph contains a vast amount of user click information, giving rise to contentindependent, unsupervised methods for many applications such as query/document clustering and classification [3, 10, 18] and query-to-document retrieval [1, 8].”
Probabilistic query expansion using query logs (2002, background): “Our previous work on using query logs to cluster similar queries also strongly supports this assumption [16].”
Semantic Matching in Search (2014, methodology): “Other algorithms are also applied to query clustering, such as K-means [5] and DBSCAN [185].”
How are we searching the World Wide Web? A comparison of nine search engine transaction logs (2006, background): “Wen, Nie, and Zhang (2001) conducted research on a Web-based version of the Encarta encyclopedia.”
A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion (2015, methodology): “Query suggestion algorithms use clustering methods to ﬁnd similar queries so that they can be used as suggestions for one another [1, 40].”
The query-flow graph: model and applications (2008, methodology): “Most of the work on query recommendation is focused on measures of query similarity [23, 11] that can be used for query expansion [3] or query clustering [3, 22].”
Query suggestion using hitting time (2008, background): “While there are quite a few work on generating different types of query suggestions, such as query auto completion [7, 8], query spelling correction [10, 15], query expansion [21, 22, 3], and query rewriting [1, 13].”