Agglomerative clustering of a search engine query log

Beeferman, Doug; Berger, Adam

doi:10.1145/347090.347176

Cited by 613 publications

(432 citation statements)

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“…Muresan and Harper (2004) propose a topic modeling system for developing mediated queries. Beeferman and Berger (2000) and Wen, et al (2002) applied query clustering that uses search engine query logs including clickthrough data, which provides the documents that the user have selected as a result of the search query. Query similarities are proposed based on the common documents that users have selected.…”

Section: Introduction and Related Researchmentioning

confidence: 99%

Investigating the performance of automatic new topic identification across multiple datasets

Özmutlu

Çavdur

Spink

et al. 2006

Proc of Assoc for Info

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Section: Introduction and Related Researchmentioning

confidence: 99%

Investigating the performance of automatic new topic identification across multiple datasets

Özmutlu

Çavdur

Spink

et al. 2006

Proc of Assoc for Info

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“…Beeferman and Berger in [4] apply a hierarchical agglomerative clustering technique to click-through data to find clusters of similar queries and similar URLs in a Lycos log. A bipartite graph is created from queries and related URLs which is iteratively clustered by choosing at each iteration the two pairs of most similar queries and URLs.…”

Section: Related Workmentioning

confidence: 99%

Incremental Algorithms for Effective and Efficient Query Recommendation

Broccolo

Nardini

Perego

et al. 2010

String Processing and Information Retrieval

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Abstract. Query recommender systems give users hints on possible interesting queries relative to their information needs. Most query recommenders are based on static knowledge models built on the basis of past user behaviors recorded in query logs. These models should be periodically updated, or rebuilt from scratch, to keep up with the possible variations in the interests of users. We study query recommender algorithms that generate suggestions on the basis of models that are updated continuously, each time a new query is submitted. We extend two stateof-the-art query recommendation algorithms and evaluate the effects of continuous model updates on their effectiveness and efficiency. Tests conducted on an actual query log show that contrasting model aging by continuously updating the recommendation model is a viable and effective solution.

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“…For example, Beeferman and Berger [2] applied agglomerative clustering techniques to bipartite click graphs using a simple set overlap distance function. Antonellis et al [1] build upon Simrank [12], a measure of structural-context similarity developed for personalized web graphs, to identify similar queries.…”

Section: Related Workmentioning

confidence: 99%

Optimizing query rewrites for keyword-based advertising

Malekian

Chang

Kumar

et al. 2008

Proceedings of the 9th ACM Conference on Electronic Commerce

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We consider the problem of query rewrites in the context of pay-per-click search advertising. Given a three-layer graph consisting of queries, query rewrites, and the corresponding ads that can be served for the rewrites, we formulate a family of graph covering problems whose goals are to suggest a subset of ads with the maximum benefit by suggesting rewrites for a given query. We obtain constant-factor approximation algorithms for these covering problems, under two versions of constraints and a realistic notion of ad benefit. We perform experiments on real data and show that our algorithms are capable of outperforming a competitive baseline algorithm in terms of the benefit of the rewrites.

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Agglomerative clustering of a search engine query log

Cited by 613 publications

References 10 publications

Investigating the performance of automatic new topic identification across multiple datasets

Investigating the performance of automatic new topic identification across multiple datasets

Incremental Algorithms for Effective and Efficient Query Recommendation

Optimizing query rewrites for keyword-based advertising

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