An Overview of Aggregating Vertical Results into Web Search Results

Vertical aggregation is the task of integrating results from specialized search services or verticals into the web search results. Aggregating verticals into the core web results helps in achieving diversity in information search. In this paper various efforts made for selecting relevant vertical and presenting the aggregated results to the users are reviewed. Various vertical selection approaches and design and evaluation of aggregated search interfaces have been discussed which has been a less focused area as compared to the most prior research work in conventional web search interfaces.

[1]  Tie-Yan Liu,et al.  Learning to Rank for Information Retrieval , 2011 .

[2]  Ziv Bar-Yossef,et al.  Efficient search engine measurements , 2007, WWW '07.

[3]  Robert Villa,et al.  Factors affecting click-through behavior in aggregated search interfaces , 2010, CIKM.

[4]  Ben Carterette,et al.  An Analysis of Assessor Behavior in Crowdsourced Preference Judgments , 2010 .

[5]  Qiang Yang,et al.  Q2C@UST: our winning solution to query classification in KDDCUP 2005 , 2005, SKDD.

[6]  Fernando Diaz,et al.  Learning to aggregate vertical results into web search results , 2011, CIKM '11.

[7]  Jakob Nielsen,et al.  Eyetracking Web Usability , 2009 .

[8]  Fernando Diaz,et al.  Integration of news content into web results , 2009, WSDM '09.

[9]  Oren Etzioni,et al.  Multi-Engine Search and Comparison Using the MetaCrawler , 1995, World Wide Web J..

[10]  Tie-Yan Liu,et al.  Learning to rank for information retrieval , 2009, SIGIR.

[11]  Fernando Diaz,et al.  A Methodology for Evaluating Aggregated Search Results , 2011, ECIR.

[12]  Edward Cutrell,et al.  An eye tracking study of the effect of target rank on web search , 2007, CHI.

[13]  Jack G. Conrad,et al.  Effective collection metasearch in a hierarchical environment: global vs. localized retrieval performance , 2002, SIGIR '02.

[14]  Jie Lu,et al.  Full-text federated search in peer-to-peer networks , 2007, SIGF.

[15]  James P. Callan,et al.  Query-based sampling of text databases , 2001, TOIS.

[16]  Ophir Frieder,et al.  Automatic classification of Web queries using very large unlabeled query logs , 2007, TOIS.

[17]  Luis Gravano,et al.  Distributed Search over the Hidden Web: Hierarchical Database Sampling and Selection , 2002, VLDB.

[18]  Qiang Yang,et al.  Building bridges for web query classification , 2006, SIGIR.

[19]  Barry Smyth,et al.  Are people biased in their use of search engines? , 2008, CACM.

[20]  Eugene Agichtein,et al.  Identifying "best bet" web search results by mining past user behavior , 2006, KDD '06.

[21]  Tapas Kanungo,et al.  On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals , 2011, WSDM '11.

[22]  Milad Shokouhi,et al.  Federated Search , 2011, Found. Trends Inf. Retr..

[23]  Xiao Li,et al.  Learning query intent from regularized click graphs , 2008, SIGIR '08.

[24]  Guijun Wang,et al.  Information fusion with ProFusion , 1996, WebNet.

[25]  Luo Si,et al.  Unified utility maximization framework for resource selection , 2004, CIKM '04.

[26]  Zheng Chen,et al.  A probabilistic model based approach for blended search , 2009, WWW '09.

[27]  Ophir Frieder,et al.  Improving automatic query classification via semi-supervised learning , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[28]  Dik Lun Lee,et al.  Server Ranking for Distributed Text Retrieval Systems on the Internet , 1997, DASFAA.

[29]  Jack G. Conrad,et al.  Early user---system interaction for database selection in massive domain-specific online environments , 2003, TOIS.

[30]  W. Bruce Croft,et al.  Cluster-based language models for distributed retrieval , 1999, SIGIR '99.

[31]  Oren Etzioni,et al.  The MetaCrawler architecture for resource aggregation on the Web , 1997 .

[32]  Marco Furini,et al.  International Journal of Computer and Applications , 2010 .

[33]  Fernando Diaz,et al.  Sources of evidence for vertical selection , 2009, SIGIR.

[34]  Thorsten Joachims,et al.  Accurately interpreting clickthrough data as implicit feedback , 2005, SIGIR '05.

[35]  Clement T. Yu,et al.  Advanced Metasearch Engine Technology , 2010, Advanced Metasearch Engine Technology.

[36]  Mounia Lalmas,et al.  Dynamics of Genre and Domain Intents , 2010, AIRS.

[37]  Fernando Diaz,et al.  Vertical selection in the presence of unlabeled verticals , 2010, SIGIR '10.

[38]  Luo Si.  Federated search of text search engines in uncooperative environments , 2007, SIGF.

[39]  Milad Shokouhi,et al.  Using query logs to establish vocabularies in distributed information retrieval , 2007, Inf. Process. Manag..

[40]  William P. Birmingham,et al.  Architecture of a metasearch engine that supports user information needs , 1999, CIKM '99.

[41]  W. Bruce Croft,et al.  Searching distributed collections with inference networks , 1995, SIGIR '95.

[42]  Luis Gravano,et al.  GlOSS: text-source discovery over the Internet , 1999, TODS.

[43]  King-Lup Liu,et al.  Building efficient and effective metasearch engines , 2002, CSUR.

[44]  Oren Etzioni,et al.  Multi-Service Search and Comparison Using the MetaCrawler , 1995 .

[45]  Luis Gravano,et al.  The Effectiveness of GlOSS for the Text Database Discovery Problem , 1994, SIGMOD Conference.

[46]  Guijun Wang,et al.  ProFusion*: Intelligent Fusion from Multiple, Distributed Search Engines , 1996, J. Univers. Comput. Sci..

[47]  George Karypis,et al.  Intelligent metasearch engine for knowledge management , 2003, CIKM '03.

[48]  Adele E. Howe,et al.  Experiences with selecting search engines using metasearch , 1997, TOIS.

[49]  Fernando Diaz,et al.  Adaptation of offline vertical selection predictions in the presence of user feedback , 2009, SIGIR.

[50]  Vijay V. Raghavan,et al.  AllInOneNews: development and evaluation of a large-scale news metasearch engine , 2007, SIGMOD '07.

[51]  Jaime Arguello.  Integrating and Ranking Aggregated Content on the Web The Theory and Practice of Aggregated Search and Whole-Page Composition , 2012 .

[52]  Weiguo Fan,et al.  Identifying vertical search intention of query through social tagging propagation , 2009, WWW '09.

[53]  Ricardo Baeza-Yates,et al.  Advanced Topics in Information Retrieval , 2011, The Information Retrieval Series.

[54]  Peter Jackson,et al.  Database Selection Using Actual Physical and Acquired Logical Collection Resources in a Massive Domain-specific Operational Environment , 2002, VLDB.

[55]  Mounia Lalmas,et al.  A Task-Based Evaluation of an Aggregated Search Interface , 2009, SPIRE.