Agglomerative clustering of a search engine query log

By Doug Beeferman, Adam L. Berger. Knowledge Discovery and Data Mining, 2000

952 citations recorded by the source corpus.

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Subfields: Information Systems, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/248280b0f0885d0da0d80934284a01d48d56a689

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Citations in the corpus

Query enrichment for web-query classification (2006, background): “2002; Beeferman and Berger 2000].”
Clustering user queries of a search engine (2001, methodology): “This second criterion has also been used in [1] to cluster user queries.”
Building bridges for web query classification (2006, methodology): “Both Beeferman [1] and Wen [14] used search engines’ clickthrough data to cluster the queries.”
Web Search Intent Induction via Automatic Query Reformulation (2004, methodology): “This is the same weakness as is found in the Beeferman and Berger (2000) approach.”
Hierarchical Clustering Algorithms for Document Datasets (2005, methodology): “The document-to-document similarity graph Gs [2]is obtained by treating the pairwise similarity matrix of the dataset as the adjacency matrix of Gs .”
Query Recommendation Using Query Logs in Search Engines (2004, methodology): “Befferman and Berger [3] also propose a query clustering technique based on distance notion (3).”
Algorithmic stemmers or morphological analysisq An evaluation (2009, methodology): “Beeferman and Berger (2000) applied an agglomerative clustering algorithm to search queries and relevant Web pages in a transaction log to discover potential query clusters.”
Query Expansion by Mining User Logs (2003, background): “Beeferman and Berger [2] exploited “clickthrough data” in clustering URLs and queries using graph-based iterative clustering technique.”
Item-based top-N recommendation algorithms (2004, methodology): “In recent years, recommender systems have been used in a number of different applications [Shardanand and Maes 1995; Hill et al. 1995; Konstan et al. 1997; Terveen et al. 1997; Schafer et al. 1999; Kitts et al. 2000; Mobasher et al. 2000; Beeferman and Berger 2000] such as recommending products a cu”
Query Expansion Techniques for Information Retrieval: a Survey (2017, neutral): “2000 [29]”
Query chains: learning to rank from implicit feedback (2005, background): “A number of papers (e.g. [3, 10, 18]) successfully learn to predict query reformulations.”
SearchTogether: an interface for collaborative web search (2007, methodology): “Examples from this line of work include systems that use query logs and clickthrough data to generate query substitutions or recommendations, such as [2] and [15].”
Web page classification: Features and algorithms (2009, background): “While simple textual similarity might be a reasonable start, a stronger measure is to consider pages that cooccur in top query results [Fitzpatrick and Dent 1997; Beeferman and Berger 2000; Glance 2000; Wen et al. 2002; Zaiane and Strilets 2002; Davison 2004].”
A Survey of Automatic Query Expansion in Information Retrieval (2012, methodology): “…queries that occurred in the same session (e.g., successive queries issued by a single user [Jones et al. 2006]) or queries that yielded similar sets of presumably relevant documents (e.g., by deploying the bipartite graph induced by queries and user clicks [Beeferman and Berger 2000]).”