Query clustering using user logs

Author:

Abstract

Query clustering is a process used to discover frequently asked questions or most popular topics on a search engine. This process is crucial for search engines based on question-answering. Because of the short lengths of queries, approaches based on keywords are not suitable for query clustering. This paper describes a new query clustering method that makes use of user logs which allow us to identify the documents the users have selected for a query. The similarity between two queries may be deduced from the common documents the users selected for them. Our experiments show that a combination of both keywords and user logs is better than using either method alone.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Science Applications,General Business, Management and Accounting,Information Systems

Reference21 articles.

1. BEEFERMAN D.AND BERGER A. 2000. Agglomerative clustering of a search engine query log. In Proceedings of the 6th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining (August). Acm Press New York NY 407-416. 10.1145/347090.347176

2. DUBES R.C.AND JAIN A. K. 1988. Algorithms for Clustering Data. Prentice-Hall Englewood Cliffs NJ.

3. ESTER M. KRIEGEL H. SANDER J. AND XU X. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. 226-231.

4. ESTER M. KRIEGEL H. SANDER J. WIMMER M. AND XU X. 1998. Incremental clustering for mining in a data warehousing environment. In Proceedings of the 24th International Conference on Very Large Data Bases 323-333.

5. FITZPATRICK L.AND DENT M. 1997. Automatic feedback using past queries: social searching? In Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press New York NY 306-312. 10.1145/258525.258597

Cited by 170 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web Archives;Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval;2023-07-18

2. Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion;Computers, Materials & Continua;2022

3. Event-based Product Carousel Recommendation with Query-Click Graph;2021 IEEE International Conference on Big Data (Big Data);2021-12-15

4. Web Page Recommender System using hybrid of Genetic Algorithm and Trust for Personalized Web Search;Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms;2021

5. Evaluating the Effectiveness of Query-Document Clustering Using the QDSM Measure;Advances in Science, Technology and Engineering Systems Journal;2020-12

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3