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Mining search engine query logs for query recommendation

Published: 23 May 2006 Publication History

Abstract

This paper presents a simple and intuitive method for mining search engine query logs to get fast query recommendations on a large scale industrial strength search engine. In order to get a more comprehensive solution, we combine two methods together. On the one hand, we study and model search engine users' sequential search behavior, and interpret this consecutive search behavior as client-side query refinement, that should form the basis for the search engine's own query refinement process. On the other hand, we combine this method with a traditional content based similarity method to compensate for the high sparsity of real query log data, and more specifically, the shortness of most query sessions. To evaluate our method, we use one hundred day worth query logs from SINA' search engine to do off-line mining. Then we analyze three independent editors evaluations on a query test set. Based on their judgement, our method was found to be effective for finding related queries, despite its simplicity. In addition to the subjective editors' rating, we also perform tests based on actual anonymous user search sessions.

References

[1]
R. Baeza-Yates, C. Hurtado, and M. Mendoza. Query recommendation using query logs in search engines. In International Workshop on Clustering Information over the Web (ClustWeb, in conjunction with EDBT), Creete, Greece, March (to apper in LNCS)., 2004.
[2]
D. Beeferman and A. Berger. Agglomerative clustering of a search engine query log. Proceedings of ACM SIGKDD International Conference, pages 407--415., 2000.
[3]
J. Wen, J. Nie, and H. Zhang. Query clustering using user logs. ACM Transactions on Information Systems, 20(1), pages 59--81., 2002.

Cited By

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  • (2023)Developing a Semantically Based Query Recommendation for an Electronic Medical Record Search Engine: Query Log Analysis and Design ImplicationsJMIR Formative Research10.2196/453767(e45376)Online publication date: 15-Sep-2023
  • (2023)A cooperative co-evolutionary genetic algorithm for query recommendationMultimedia Tools and Applications10.1007/s11042-023-15585-683:4(11461-11491)Online publication date: 29-Jun-2023
  • (2023)DeepQFM: a deep learning based query facets mining methodInformation Retrieval Journal10.1007/s10791-023-09427-026:1-2Online publication date: 30-Oct-2023
  • Show More Cited By

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Jean-Pierre E Norguet

An original approach for search engine query recommendation based on query log analysis is presented in this paper. The analysis process proposed in the approach is composed of three steps: query log parsing, query sequence identification, and query similarity computation. The originality of the approach is that it combines query sequence analysis and query similarity computation. This combination improves the precision rate of results over traditional query log analysis. This improvement has been measured on real data using both subjective and objective evaluations. Subjective evaluation has been conducted by collecting editors' feedback on query results. Objective evaluation has been conducted by comparing the query recommendations produced against a set of actual query sessions. Many enhancements could be made to the approach. First of all, damping factors used in the similarity graphs could rely more on existing measures from the literature, like association measures, scalar measures, and metrics measures [1]. Second, the assumption that used queries are more relevant to future users should be tested and quantified. Finally, the results should be compared to those obtained using the existing approaches. These enhancements could be combined with those already mentioned in the future work section. In general, this paper will be interesting to search engine researchers and developers. General prerequisites in information retrieval are needed to understand the similarity computation. Prerequisites in Web usage mining are also needed to understand the query log analysis process. Online Computing Reviews Service

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cover image ACM Conferences
WWW '06: Proceedings of the 15th international conference on World Wide Web
May 2006
1102 pages
ISBN:1595933239
DOI:10.1145/1135777
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

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Publication History

Published: 23 May 2006

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Author Tags

  1. mining
  2. query logs
  3. recommendation
  4. session

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2023)Developing a Semantically Based Query Recommendation for an Electronic Medical Record Search Engine: Query Log Analysis and Design ImplicationsJMIR Formative Research10.2196/453767(e45376)Online publication date: 15-Sep-2023
  • (2023)A cooperative co-evolutionary genetic algorithm for query recommendationMultimedia Tools and Applications10.1007/s11042-023-15585-683:4(11461-11491)Online publication date: 29-Jun-2023
  • (2023)DeepQFM: a deep learning based query facets mining methodInformation Retrieval Journal10.1007/s10791-023-09427-026:1-2Online publication date: 30-Oct-2023
  • (2021)You ask “Durian”, I gave “Musang King, Red Prawn and D24”: A Query Reformulation Behavior Experiment2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)10.1109/AiDAS53897.2021.9574131(1-4)Online publication date: 8-Sep-2021
  • (2021)Location‐Aware Keyword Query Suggestion Techniques With Artificial Intelligence PerspectiveComputational Analysis and Deep Learning for Medical Care10.1002/9781119785750.ch2(35-51)Online publication date: 13-Aug-2021
  • (2020)Evolution and impact of bias in human and machine learning algorithm interactionPLOS ONE10.1371/journal.pone.023550215:8(e0235502)Online publication date: 13-Aug-2020
  • (2020)Personalized query recommendation system : A genetic algorithm approachJournal of Interdisciplinary Mathematics10.1080/09720502.2020.173196423:2(523-535)Online publication date: 12-May-2020
  • (2020)Toward action comprehension for searchingJournal of the Association for Information Science and Technology10.1002/asi.2422071:2(143-157)Online publication date: 1-Jan-2020
  • (2019)Metapath-guided Heterogeneous Graph Neural Network for Intent RecommendationProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330673(2478-2486)Online publication date: 25-Jul-2019
  • (2019)Web Search Using Automatically Generated Facets2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)10.1109/ICICICT46008.2019.8993242(37-40)Online publication date: Jul-2019
  • Show More Cited By

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