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Challenges in running a commercial search engine

Published: 15 August 2005 Publication History

Abstract

These are exciting times for Information Retrieval. Web search engines have brought IR to the masses. It now affects the lives of hundreds of millions of people, and growing, as Internet search companies launch ever more products based on techniques developed in These are exciting times for Information Retrieval. Web search engines have brought IR to the masses. It now affects the lives of hundreds of millions of people, and growing, as Internet search companies launch ever more products based on techniques developed in IR research.The real world poses unique challenges for search algorithms. They operate at unprecedented scales, and over a wide diversity of information. In addition, we have entered an unprecedented world of "Adversarial Information Retrieval". The lure of billions of dollars of commerce, guided by search engines, motivates all kinds of people to try all kinds of tricks to get their sites to the top of the search results.What techniques do people use to defeat IR algorithms? What are the evaluation challenges for a web search engine? How much impact has IR had on search engines? How does Google serve over 250 Million queries a day, often with sub-second response times? This talk will show that the world of algorithm and system design for commercial search engines can be described by two of Murphy's Laws: a) If anything can go wrong, it will, and b) If anything cannot go wrong, it will anyway.

Cited By

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  • (2020)FoundationsInformation Retrieval: A Biomedical and Health Perspective10.1007/978-3-030-47686-1_1(1-39)Online publication date: 23-Jul-2020
  • (2019)Detecting Web Spam in Webgraphs with Predictive Model Analysis2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006282(4299-4308)Online publication date: Dec-2019
  • (2012)Fighting against web spamProceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval10.1145/2348283.2348338(395-404)Online publication date: 12-Aug-2012
  • Show More Cited By

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cover image ACM Conferences
SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
August 2005
708 pages
ISBN:1595930345
DOI:10.1145/1076034
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 15 August 2005

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Cited By

View all
  • (2020)FoundationsInformation Retrieval: A Biomedical and Health Perspective10.1007/978-3-030-47686-1_1(1-39)Online publication date: 23-Jul-2020
  • (2019)Detecting Web Spam in Webgraphs with Predictive Model Analysis2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006282(4299-4308)Online publication date: Dec-2019
  • (2012)Fighting against web spamProceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval10.1145/2348283.2348338(395-404)Online publication date: 12-Aug-2012
  • (2008)Telephone Call Network Data Mining: A Survey with ExperimentsHandbook of Large-Scale Random Networks10.1007/978-3-540-69395-6_12(489-530)Online publication date: 2008
  • (2007)An analysis of two approaches in information retrieval: From frameworks to study designsJournal of the American Society for Information Science and Technology10.1002/asi.2058958:7(971-986)Online publication date: 26-Mar-2007
  • (2006)Link spam detection based on mass estimationProceedings of the 32nd international conference on Very large data bases10.5555/1182635.1164166(439-450)Online publication date: 1-Sep-2006
  • (2006)Minimal test collections for retrieval evaluationProceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval10.1145/1148170.1148219(268-275)Online publication date: 6-Aug-2006

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