We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

An Overview of Aggregating Vertical Results into Web Search Results

by Suhel Mustajab, Mohd. Kashif Adhami, Rashid Ali
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 17
Year of Publication: 2013
Authors: Suhel Mustajab, Mohd. Kashif Adhami, Rashid Ali
10.5120/12063-8107

Suhel Mustajab, Mohd. Kashif Adhami, Rashid Ali . An Overview of Aggregating Vertical Results into Web Search Results. International Journal of Computer Applications. 69, 17 ( May 2013), 21-28. DOI=10.5120/12063-8107

@article{ 10.5120/12063-8107,
author = { Suhel Mustajab, Mohd. Kashif Adhami, Rashid Ali },
title = { An Overview of Aggregating Vertical Results into Web Search Results },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 17 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 21-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number17/12063-8107/ },
doi = { 10.5120/12063-8107 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:30:31.746980+05:30
%A Suhel Mustajab
%A Mohd. Kashif Adhami
%A Rashid Ali
%T An Overview of Aggregating Vertical Results into Web Search Results
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 17
%P 21-28
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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.

References
  1. F. Diaz. Integration of news content into web results. In WSDM 2009, 2009.
  2. J. Arguello, F. Diaz, J. Callan, and J. -F. Crespo. : Sources of evidence for vertical selection. In SIGIR09, pages 315-322, 2009.
  3. F. Diaz and J. Arguello. Adaptation of online vertical selection predictions in the presence of user feedback. In SIGIR 2009, pages 323{330. ACM, 2009.
  4. J Arguello, F. Diaz, and J. -F. Paiement. Vertical selection in the presence of unlabeled verticals. In SIGIR 2010, Pages 691-698. ACM, 2010.
  5. L. Gravano, C. Chang, H. Garca-Molina and A. Paepcke. STARTS: Stanford proposai for internet metasearching. SIGMOID, pages 207-218, T1997.
  6. M. Shokouhi and L. Si. : Federated search. Foundations and Trends in Information Retrieval, 5(1):1-102, 2011.
  7. J. Callan and M. Connell. : Query-based sampling of text databases. ACM Trans. Inf. Syst. , 19:97-130, 2001.
  8. J. P. Callan, Z. Lu, and W. B. Croft. : Searching distributed collections with inference networks. In SIGIR95, pages 21-28, 1995.
  9. M. Shokouhi, J. Zobel, S. Tahaghoghi and F. Scholer. Using query logs to establish vocabularies in distributed information retrieval. IP&M, 43(1):169180, 2007.
  10. B. Yuwono and D. L. Lee. Server Ranking for Distributed text Retrieval Systems on the internet. In DASFAA 1997, pages 1-50. World Scientefic Press, 1997.
  11. J. Xu and W. B. Croft. Cluster-based language models for distributed retrieval. In SIGIR 1999, pages 254-261. ACM, 1999.
  12. D. Shen, J. -T. Sun, Q. Yang, and Z. Chen. Building bridges for web query classification. In SIGIR 2006, pages 131-138, 2006.
  13. S. M. Beitzel, E. C. Jensen, D. D. Lewis, A. Chowdhury, and O. Frieder. Automatic classification of web queries using very large unlabled query logs. TOIS, 25(2):9, 2007.
  14. S. M. Beitzel, E. C. Jensen, O. Frieder, D. D. Lewis, A. Chowdhury and A. Kolcz. Improving automatic query classificationvia semi-supervised learning. In ICDM 2005, pages 42-49, 2005.
  15. L. Gravano, H. Garca_molina, A. Tomasic, I. Rocquencourt and N. L. Gravano. Gloss: Text_source discovery over the internet. Transactions on Database systems, 24: pages 229-264, 1999.
  16. ] M. Lalmas, Advanced topics in information retrieval, The Information Retrieval (Eds) M. Melucci, R. Baeza-Yates 2011, XXX, 306 p. 87 illus. , Hardcover ISBN: 978-3-642-29945-1
  17. A. K. Ponnuswami, K. Pattabiraman, Q. Wu, R. Gilad-Bachrach, and T. Kanungo. On composition of a federated web search result page: Using online users to provide pairwise preference for heterogeneous verticals. In WSDM 2011, pages 715{724. ACM, 2011.
  18. J. Arguello, F. Diaz, and J. Callan. Learning to aggregate vertical results into web search results. CIKM, 2011.
  19. L. Si and J. Callan. Relevant document distribution estimation method for resource selection. In SIGIR 2003, pages 298-305, 2003.
  20. X. Li, Y. –Y. Wang and A. Acero. Learning query intent from regularized click graphs. In SIGIR 2008, pages 339-346. ACM 2008.
  21. N. Liu, J. Yan, and Z. Chen. A Probabilistic Model based Approach for Blended Search. WWW 2009, pages 20-24. ACM 2009.
  22. J. Arguello, F. Diaz, J. Callan, and B. Carterette. : A methodology for evaluating aggregated search results. In ECIR11, pages 141-152, 2011.
  23. A. C. Konig, M. Gamon, and Q. Wu. Click-through production for news queries. In SIGIR 2009, pages 347-354. ACM 2009.
  24. T. –Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3:pages 225-331, 2009.
  25. L. Gravano, H. Garcia_Molina, and A. Tomasic. The effectiveness of GIOSS for the text database discovery problem. In SIGMOID 1994, ACM 1994.
  26. L. Si. Federated search of text search engines in uncooperative environments. Phd. Thesis, Carnegie Mellon University, Pittsbiurgh, PA, 2006.
  27. D. Shen, R. Pan, J. –T. Sun, J. J. Pan, K. Wu, J. Yin, and Q. Yang. Q2c@ust: our winning solution to query classification in kddcup 2005. In SIGKDD Explor. Newsl. , 7(2): pages 100-110, 2005.
  28. S. Sushmita, H. Joho, M. Lalmas and R. Villa. Factors Affecting Click-Through Behavior in Aggregated Search Interfaces. In CIKM, pages 519-528, 2010.
  29. D. Zhu and B. Carterette. An analysis of assessor behavior in crowdsourced preference judgements. In SIGIR , pages 21-26, ACM 2010.
  30. W. Y. Meng and C. Yu, Advanced Metasearch Engine Technology. Morgan & Claypool Publishers, 2010. ISBN 1608451925.
  31. E. Selberg and O. Etzioni, "Multi-service search and comparison using the metacrawler," in Proceedings of the Fourth International Conference on World Wide Web, Boston, MA: Oreilly, 1995. ISBN 978-1-56592-169-6.
  32. E. Selberg and O. Etzioni, "The MetaCrawler architecture for resource aggregation on the web," IEEE Expert, vol. 12, no. 1, pages. 8–14, ISSN 0885-9000, 1997.
  33. S. Gauch and G. Wang, "Information fusion with ProFusion," in Proceedings of the First World Conference of the Web Society, pages. 174–179, San Francisco, CA, 1996.
  34. S. Gauch, G. Wang, and M. Gomez, "ProFusion: Intelligent fusion from multiple distributed search engines," Journal of Universal Computer Science, vol. 2, no. 9, pages. 637–649, ISSN 0948-695X, 1996.
  35. K. Liu, W. Meng, J. Qiu, C. Yu, V. Raghavan, Z. Wu, Y. Lu, H. He, and H. Zhao, "Allinonenews: development and evaluation of a large-scale news metasearch engine," in Proceedings of the ACM SIGMOD International Conference on Management of Data, (C. Y. Chan, B. C. Ooi, and A. Zhou, eds. ), pages. 1017–1028, Beijing, China, 2007. ISBN 978-1-59593-686-8.
  36. E. Han, G. Karypis, D. Mewhort, and K. Hatchard, "Intelligent metasearch engine for knowledge management," in Kraft et al.
  37. , pages 492–495, 2003. ISBN 1-58113-723-0.
  38. Z. Bar-Yossef and M. Gurevich, "Efficient search engine measurements," in Williamson et al.
  39. , pages 401–410. ISBN 978-1-59593-654-7.
  40. D. Dreilinger and A. Howe, "Experiences with selecting search engines using metasearch," ACM Transaction on Information Systems, vol. 15, no. 3, pages 195–222, ISSN 1046-8188, 1997.
  41. A. Smeaton and F. Crimmins, "Using a data fusion agent for searching the WWW," in selected papers from the sixth international conference on world wide web. CA Elsevier, 1997.
  42. E. Glover, S. Lawrence, W. Birmingham, and C. Giles, "Architecture of a metasearch engine that supports user information needs," in Gauch
  43. , pages 210–216, 1999. ISBN 1-58113-1461
  44. W. Meng, C. Yu, and K. Liu, "Building efficient and effective metasearch engines," ACM Computing Surveys, vol. 34, no. 1, pages 48–89, ISSN 0360-0300, 2002.
  45. J. Arguello, F. Diaz, and M. Shokouhi. Integrating and Ranking Aggregated Content on the Web. 1UNC Chapel Hill2Yhoo! Labs 3Microsoft Research, 2012.
  46. J. Lu. "Full-text federated search in peer-to-peer networks," Phd thesis, Carnegie Mellon University, 2007.
  47. J. Conrad and J. Claussen, "Early user — system interaction for database selection in massive domain-specific online environments," ACM Transactions on Information Systems, vol. 21, no. 1, pages 94–131, ISSN 1046-8188, 2003.
  48. J. Conrad, X. Guo, P. Jackson, and M. Meziou. Database selection using actual physical and acquired logical collection resources in a massive domain specific operational environment, in Bernstein et al.
  49. , pages 71-82.
  50. J. Conrad, C. Yang, and J. Claussen, "Effective collection metasearch in a hierarchical environment: global vs. localized retrieval performance," in J¨arvelin t al.
  51. . ISBN 1-58113-561-0.
  52. Naver Search Engine. http//www. naver. com/.
  53. A. Aula and K. Rodden. Eye-tracking studies: more than meets the eye. June 2009.
  54. Kosmix Search Engine. http//www. kosmix. com/.
  55. J. Neilsen. Eyetracking Web Usability. Kara Pernice, 2009.
  56. G. Hotchkiss. Eye Tracking on Universal and Personalized Search. http://searchengineland. com/eye-trackink-on-universal-and-personalized-search-12233, September 2007.
  57. iProspect Blended Search Result Study. http://www. iprospect. com/about /researchstudy_2008_blendedsearchresults. htm, April 2008.
  58. S. Sushmita, H. Joho and M. Lalmas. A Task-Based Evaluation of an Aggregated Search Interface. SPIRE, pages 322-333, 2009.
  59. S. Sushmita, B. Piwowarski and M. Lalmas. Dynamics of Genre and Domain Intents, AIRS, 2010.
  60. E. Goodman and E. Feldblum. Blended Search and the New Rules of Engagement. ComScore Report, October 2010.
  61. T. Joachims, L. Granka, B. Pan, H. Hembrooke and G. Gay. Accurately interpreting clickthrough data as implicit feedback. ACM SIGIR, pages 154–161, 2005.
  62. E. Agichtein and Z. Zheng. Identifying "best bet" web search results by mining past user behavior. ACM SIGKDD, pages 902–908, 2006.
  63. ] Z. Guan and E. Cutrell An eye tracking study of the effect of target rank on web search. ACM SIGCHI, pages 417–420, 2007.
  64. M. T. Keane, M. O'Brien and B. Smyth Are people biased in their use of search engines? CACM, 51(2):49–52, 2008.
  65. P. Ipeirotis and L. Gravano. Distributed search over the hidden web: Hierarchical database sampling and selection. In proceedings of the 28th International Conference on Very Large Databases (VLDB), 2002.
  66. L. Si and J. Callan. Unified utility maximization framework for resource selection. In proceedings of the ACM CIKM International Conference on Information and Knowledge Management, pages 32-41, Washington, DC, 2004. ISBN 1-58113-874-1.
  67. N. Liu, J. Yan, W. Fan, Q. Yang, and Z. Chen. Identifying Vertical Search Intention of Query through Social Tagging Propagation. WWW 2009, pages 20-24, ACM 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Verticals resource selection aggregated search vertical selection web-page ranking

Powered by PhDFocusTM