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Query clustering using click-through graph

Jeonghee Yi, Farzin Maghoul
2009 Proceedings of the 18th international conference on World wide web - WWW '09  
In this paper we describe a problem of discovering query clusters from a click-through graph of web search logs.  ...  A cluster of queries is formed from the queries in a biclique. We present a scalable algorithm that enumerates all maximal bicliques from the click-through graph.  ...  We represent the query and click-through page relationships by a directed bipartite graph that consists of a set of queries, a set of Figure 1 : A hypothetical click-through graph web page URLs, and a  ... 
doi:10.1145/1526709.1526853 dblp:conf/www/YiM09 fatcat:hcjcaigcu5eodd4o6pthtvd5sq

ECO: Event Detection from Click-through Data via Query Clustering [chapter]

Prabhu K. Angajala, Sanjay K. Madria, Mark Linderman
2012 Lecture Notes in Computer Science  
The problem of event detection is transformed into query clustering by generating clustershybrid cover graphs; each hybrid cover graph corresponds to a real-world event.  ...  the semantics, structure, and content of queries and pages (iii) aims to achieve the overall objective via Query Clustering.  ...  Later their work in [3] laid a foundation for visitor-centric approach to detect events by using click-through data. The query-page relationship is represented as the vector-based graph.  ... 
doi:10.1007/978-3-642-33606-5_20 fatcat:dqh5sqipqbdafab7ygeuuqw3v4

Event detection from evolution of click-through data

Qiankun Zhao, Tie-Yan Liu, Sourav S. Bhowmick, Wei-Ying Ma
2006 Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06  
After that, the vector-based graph is transformed into its dual graph, where each node is a query-page pair that will be used to represent real world events.  ...  Given the click-through data, in our proposed approach, we first segment it into a sequence of bipartite graphs based on the user-defined time granularity.  ...  In [15] , the click-through data is used to improve the content-based query clustering for the application of FAQ identification.  ... 
doi:10.1145/1150402.1150456 dblp:conf/kdd/ZhaoLBM06 fatcat:yb77lkl7gvc53met4xizwgjmhu

Mining query log graphs towards a query folksonomy

Alexandre P. Francisco, Ricardo Baeza-Yates, Arlindo L. Oliveira
2011 Concurrency and Computation  
We tackle the problem of clustering click induced graphs, namely we discuss an efficient hierarchical clustering method for these large weighted graphs.  ...  Our results rely on the analysis of large query log induced graphs, namely click induced graphs.  ...  [11, 12, 13, 14] also uses query logs to build a query taxonomy to also cluster answers. However they do not use any user feedback, like user clicks.  ... 
doi:10.1002/cpe.1773 fatcat:47gh2ch5unbe3naicqrnogpb6y

A Survey on Query Suggestion

Lingling Meng
2014 International Journal of Hybrid Information Technology  
Adjacency based query suggestion, co-occurrence based query suggestion, query-flow graph based query suggestion, clustering based query suggestion, and bipartite graph based query suggestion are presented  ...  Sadikov [37] combined the query-flow graphs with click URLs information to find query suggestions.  ...  Ma [56] established a user-query bipartite graph and a query-URL bipartite graph based on click-through.  ... 
doi:10.14257/ijhit.2014.7.6.04 fatcat:wyno36a6yfdo3pnhgz2exzbbmu

Top-K Search Query Grouping using SOM Clustering for Search Engine

Sami Uddin, Amit Kumar Nandanwar
2015 International Journal of Computer Applications  
We propose a novel similarity matrix for user queries by uses of URL clicked by user trough searching results.  ...  This paper gives a novel clustering approach based on to identify query similarity and apply SOM clustering for effective clustering results.  ...  Click-through Bipartite A click-through bipartite graph, like Figure a pair of, summarizes click relations between queries and URLs in searches.  ... 
doi:10.5120/19210-0984 fatcat:r5bp2ty2lnhcfh76mhy4gmkxza

Query Recommendation for Long Tail Queries-A Review Paper

Anand PrasadGupta, Sunita Yadav
2012 International Journal of Computer Applications  
Query recommendation methods are powerful technique to generate related queries or alternate queries as a query suggestion for original query which is given by user in the search engine first time.  ...  Some query recommendation method do not covers the unseen and rare queries but some of them covers these queries by some additional feature added such as generalize the query token of input query by a  ...  used a "content-ignorant" approach and a graph-based iterative clustering method was used to cluster both the URLs and queries [4] .  ... 
doi:10.5120/4880-7315 fatcat:novefvdflbaotiw6xfyo6key5m

Learning query intent from regularized click graphs

Xiao Li, Ye-Yi Wang, Alex Acero
2008 Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '08  
This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a unified user interface  ...  Specifically, we infer class memberships of unlabeled queries from those of labeled ones according to their proximities in a click graph.  ...  We are grateful to Jigar Mody, Samir Lakhani, Amit Gupta, Srinivas Bobba and Misha Mikhailov for providing data and for useful discussions.  ... 
doi:10.1145/1390334.1390393 dblp:conf/sigir/LiWA08 fatcat:bwxyome4f5egvdudrpgxohj5lu

Exploiting user clicks for automatic seed set generation for entity matching

Xiao Bai, Flavio P. Junqueira, Srinivasan H. Sengamedu
2013 Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13  
We use random walk with restart to reduce data sparseness, rely on co-clustering to group queries and Web pages, and exploit page similarity to improve matching precision.  ...  The key insight of our approach is that Web pages clicked for a given query are likely to be about the same entity.  ...  In [9] , pages relevant to a query are ranked based on user clicks through forward and backward random walks on the click graph.  ... 
doi:10.1145/2487575.2487662 dblp:conf/kdd/BaiJS13 fatcat:xttabuoxubhs5cym5hl2nawoci

A structured approach to query recommendation with social annotation data

Jiafeng Guo, Xueqi Cheng, Gu Xu, Huawei Shen
2010 Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10  
Based on the query relation graph, we employ hitting time to rank possible recommendations, leverage a modularity based approach to group top recommendations into clusters, and label each cluster with  ...  query recommendation, we attract more user clicks on recommendations. This type of query recommendation has not been explicitly addressed in previous work.  ...  [28] proposed a clustering method for query recommendation that combine query content and click-through information.  ... 
doi:10.1145/1871437.1871518 dblp:conf/cikm/GuoCXS10 fatcat:m3vjwpqmbfgfnb6rhdzcgayg3m

Mining Large Query Induced Graphs towards a Hierarchical Query Folksonomy [chapter]

Alexandre P. Francisco, Ricardo Baeza-Yates, Arlindo L. Oliveira
2010 Lecture Notes in Computer Science  
Our approach consists on efficiently obtaining a hierarchical clustering for such graphs and, then, a hierarchical query folksonomy.  ...  In this paper we present and discuss results on mining large query log induced graphs, and how they contribute to query classification and to understand user intent and interest.  ...  This notion uses common clicked URLs and it was introduced by Baeza-Yates and Tiberi [3] . Chuang et al. [5, 6, 7] also used query logs to build a query taxonomy to also cluster answers.  ... 
doi:10.1007/978-3-642-16321-0_24 fatcat:mq2r7uuv4bba7m42zo6d535kyy

Query Suggestion and Recommendation Using Bipartite Graph and K-Means Clustering
IJARCCE - Computer and Communication Engineering

2014 IJARCCE  
out query suggestion and recommends query and URL"s using Bipartite graph and Kmeans clustering.  ...  Innumerable different kinds of recommendations are made on the Web every day, including movies, music, images, books recommendations, query suggestions, tags recommendations, etc., The proposed work carries  ...  In many scenarios, such suggestions can be generated from a large scale graph of queries and other accessory information, such as the click through.  ... 
doi:10.17148/ijarcce.2014.31137 fatcat:znxk3mwojjhnxb2qlibfa2imjq

A Survey on Feedback Session for Inferring User Search Query with CAP

2016 International Journal of Science and Research (IJSR)  
the proposed feedback session .feedback session buit/ construct from user click through logs consist the information about different user search information.  ...  to infer user search goals by analyzing search engine queries. we propose 2 different framework .first we propose framework to find out different user search information for user query by using clustering  ...  The important objective is to improve amount of data by using supervised study with click graphs. Based on the click graph, unnamed queries are produced from labeled queries.  ... 
doi:10.21275/v5i1.nov152863 fatcat:bsipuv6kdrbjdjn5sikblsv5ki

Search Query Expansion using Genetic Algorithm?based Clustering

D. Indumathi
2013 The Smart Computing Review  
The main contributions of this paper are as follows:  A user concept preference profile is generated based on extracted concepts and user click-through data.  ...  Formation of initial clusters using agglomerative clustering is described in Section 5. Clustering using the genetic algorithm is explained in Section 6. Experimental results are shown in Section 7.  ...  To resolve the disadvantage of keyword-based clustering methods, click-through data has been used to cluster queries based on common clicks on URLs [9] .  ... 
doi:10.6029/smartcr.2013.01.002 fatcat:fifxdgrnr5gilo5cu3mnx6xjny

Clustering user queries into conceptual space

Li-Chin Lee, Yi-Shin Chen
2013 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference  
We propose a framework to extract semantic concepts by grouping queries using a clustering technique.  ...  In addition, we build hierarchies among concepts by splitting and merging clusters iteratively.  ...  Beeferman and Berger [5] applied an iterative agglomerative clustering algorithm to a bipartite graph built from queries and clicked URLs.  ... 
doi:10.1109/apsipa.2013.6694197 dblp:conf/apsipa/LeeC13 fatcat:cmfgdwtwnrcmdf4mzjmkoaurby
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