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Inferring semantic query relations from collective user behavior

Published: 26 October 2008 Publication History

Abstract

In this paper we describe how high quality transaction data comprising of online searching, product viewing, and product buying activity of a large online community can be used to infer semantic relationships between queries. We work with a large scale query log consisting of around 115 million queries from eBay. We discuss various techniques to infer semantic relationships among queries and show how the results from these methods can be combined to measure the strength and depict the kinds of relationships. Further, we show how this extraction of relations can be used to improve search relevance, related query recommendations, and recovery from null results in an eCommerce context.

References

[1]
Metzler D., Dumais S. and Meek C. Similarity measures for short segments of text. Advances in Information Retrieval (2007), pp. 16--27.
[2]
Beeferman D. and Berger A. Agglomerative clustering of a search engine query log. Proceedings of the sixth ACM SIGKDD international conference, Boston, Massachusetts, United States, Pages 407--416, 2000.
[3]
Gabrilovich E. and Markovitch S. Computing semantic relatedness using Wikipedia--based explicit semantic analysis. In proceedings of the Twentieth International Joint Conference for Artificial Intelligence, pages 1601--1611, Hyderabad, India, 2007.
[4]
Sahami M. and Heilman T. A web-based kernel function for measuring the similarity of short text snippets. WWW 2006, May 23--26, 2006, Edinburgh, Scotland.
[5]
Baeza Yates R. and Tiberi A. Extracting semantic relations from query logs. Proceedings of the 13th ACM SIGKDD conference, San Jose, California, USA, Pages 76--85, 2007.
[6]
Bollegala D., Matsuo Y. and Ishizuka M. Measuring semantic similarity between words using web search engines. Proceedings of the 16th international conference on World Wide Web, Banff, Alberta, Canada, Pages 757--766, 2007.
[7]
Carterette B., Jones R., Greiner W. and Barr C. N semantic classes are harder than two. Proceedings of the COLING/ACL on Main conference poster sessions, Sydney, Australia, Pages 49--56, 2006.
[8]
Shi X. and Yang C. Mining related queries from search engine query logs. Proceedings of the 15th international conference on World Wide Web, Edinburgh, Scotland, Pages 943--944, 2006.
[9]
Silverstein R., Helzinger M., Marais H. and Moricz M. Analysis of a very large AltaVista query log. SRC Technical Note, 1998--014, October 26, 1998.
[10]
Cucerzan S. and Brill E. Extracting semantically related queries by exploiting user session information. Technical Report, Microsoft Research, 2005.
[11]
Chien S. and Immorlica N. Semantic similarity between search engine queries using temporal correlation. Proceedings of the 14th international conference on World Wide Web, Chiba, Japan, Pages 2--11, 2005.
[12]
Vlachos M., Meek C., Vagena Z. and Gunupulos D. Identifying similarities, periodicities and bursts for online search queries. Proceedings of the 2004 ACM SIGMOD conference, Paris, France, Pages 131--142, 2004.
[13]
Gupta R. Query representation in a space defined by item features. Technical Report, eBay Research Labs, 2007.
[14]
Clibrasi R. and Vitanyi P. The Google similarity distance. IEEE Transactions on Knowledge and Data Engineering, March 2007 (Vol. 19, No. 3) pp. 370--383.
[15]
Salton G. and McGill M. Introduction to Modern Information Retrieval. McGraw-Hill Inc. New York, USA.
[16]
Frakes W. and Baeza-Yates R. Information Retrieval: Data Structures & Algorithms, Prentice-Hall.
[17]
Fang H., Tao T. and Zhai C. A formal study of information retrieval heuristics. Proceedings of the 27th international ACM SIGIR conference, Sheffield, U.K., Pages 49--56, 2004.
[18]
Fonesca B., Golgher P., Possas B., Ribeiro-Neto B. and Ziviani N. Concept-based interactive query expansion. Proceedings of the 14th ACM CIKM Conference, Breman, Germany, Pages 696--703, 2005.
[19]
Landauer T., Foltz P. and Laham D. Introduction to Latent Semantic Analysis, Discourse Processes, 25, 259--284 (1998).

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cover image ACM Conferences
CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge management
October 2008
1562 pages
ISBN:9781595939913
DOI:10.1145/1458082
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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Publication History

Published: 26 October 2008

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Author Tags

  1. graph mining
  2. query log mining
  3. semantic query networks
  4. semantic relatedness

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CIKM08
CIKM08: Conference on Information and Knowledge Management
October 26 - 30, 2008
California, Napa Valley, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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  • (2021)How Do Users Revise Zero-Hit Product Search Queries?Advances in Information Retrieval10.1007/978-3-030-72240-1_14(185-192)Online publication date: 30-Mar-2021
  • (2019)Modeling User Actions in Job SearchAdvances in Information Retrieval10.1007/978-3-030-15712-8_42(652-664)Online publication date: 7-Apr-2019
  • (2018)Social SearchSocial Information Access10.1007/978-3-319-90092-6_7(213-276)Online publication date: 3-May-2018
  • (2016)A Topic Transition Map for Query Expansion: A Semantic Analysis of Click-Through Data and Test CollectionsAI 2016: Advances in Artificial Intelligence10.1007/978-3-319-50127-7_57(648-664)Online publication date: 5-Dec-2016
  • (2014)A Study of Query Term Deletion Using Large-Scale E-commerce Search LogsProceedings of the 36th European Conference on IR Research on Advances in Information Retrieval - Volume 841610.5555/2964060.2964173(235-246)Online publication date: 13-Apr-2014
  • (2014)A Study of Query Term Deletion Using Large-Scale E-commerce Search LogsAdvances in Information Retrieval10.1007/978-3-319-06028-6_20(235-246)Online publication date: 2014
  • (2013)Query Recommendations for OLAP Discovery-Driven AnalysisDevelopments in Data Extraction, Management, and Analysis10.4018/978-1-4666-2148-0.ch004(66-90)Online publication date: 2013
  • (2013)On segmentation of eCommerce queriesProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2505721(1137-1146)Online publication date: 27-Oct-2013
  • (2012)Rewriting null e-commerce queries to recommend productsProceedings of the 21st International Conference on World Wide Web10.1145/2187980.2187989(73-82)Online publication date: 16-Apr-2012
  • (2011)Query Recommendations for OLAP Discovery-Driven AnalysisInternational Journal of Data Warehousing and Mining10.4018/jdwm.20110401017:2(1-25)Online publication date: 1-Apr-2011
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