research-article

Processing Long Queries Against Short Text: Top-k Advertisement Matching in News Stream Applications

Authors:

Dongxiang Zhang,

Heng Tao ShenAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 35, Issue 3

Article No.: 28, Pages 1 - 27

https://doi.org/10.1145/3052772

Published: 12 May 2017 Publication History

Abstract

Many real applications in real-time news stream advertising call for efficient processing of long queries against short text. In such applications, dynamic news feeds are regarded as queries to match against an advertisement (ad) database for retrieving the k most relevant ads. The existing approaches to keyword retrieval cannot work well in this search scenario when queries are triggered at a very high frequency. To address the problem, we introduce new techniques to significantly improve search performance. First, we devise a two-level partitioning for tight upper bound estimation and a lazy evaluation scheme to delay full evaluation of unpromising candidates, which can bring three to four times performance boosting in a database with 7 million ads. Second, we propose a novel rank-aware block-oriented inverted index to further improve performance. In this index scheme, each entry in an inverted list is assigned a rank according to its importance in the ad. Then, we introduce a block-at-a-time search strategy based on the index scheme to support a much tighter upper bound estimation and a very early termination. We have conducted experiments with real datasets, and the results show that the rank-aware method can further improve performance by an order of magnitude.

References

[1]

Vo Ngoc Anh, Owen de Kretser, and Alistair Moffat. 2001. Vector-space ranking with effective early termination. In Proceedings of the 2001 SIGIR Conference (SIGIR’01). 35--42.

Digital Library

[2]

Vo Ngoc Anh and Alistair Moffat. 2005. Simplified similarity scoring using term ranks. In Proceedings of the 2005 SIGIR Conference (SIGIR’05). 226--233.

Digital Library

[3]

Vo Ngoc Anh and Alistair Moffat. 2006. Pruned query evaluation using pre-computed impacts. In Proceedings of the 2006 SIGIR Conference (SIGIR’06). 372--379.

Digital Library

[4]

Nima Asadi and Jimmy Lin. 2013. Fast candidate generation for real-time tweet search with Bloom filter chains. ACM Transactions on Information Systems 31, 3, Article No. 13.

Digital Library

[5]

H. Bast, Debapriyo Majumdar, Ralf Schenkel, Martin Theobald, and Gerhard Weikum. 2006. IO-Top-k: Index-access optimized top-k query processing. In Proceedings of the 2006 VLDB Conference (VLDB’06). 475--486.

[6]

Srikanta J. Bedathur, Klaus Berberich, Jens Dittrich, Nikos Mamoulis, and Gerhard Weikum. 2010. Interesting-phrase mining for ad-hoc text analytics. Proceedings of the VLDB Endowment 3, 1, 1348--1357.

Digital Library

[7]

David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3, 993--1022.

Digital Library

[8]

Andrei Z. Broder, David Carmel, Michael Herscovici, Aya Soffer, and Jason Y. Zien. 2003. Efficient query evaluation using a two-level retrieval process. In Proceedings of the 2003 CIKM Conference (CIKM’03). 426--434.

Digital Library

[9]

C. Buckley and A. F. Lewit. 1985. Optimization of inverted vector searches. In Proceedings of the 1985 SIGIR Conference (SIGIR’85). 97--110.

Digital Library

[10]

Michael Busch, Krishna Gade, Brian Larson, Patrick Lok, Samuel Luckenbill, and Jimmy Lin. 2012. Earlybird: Real-time search at Twitter. In Proceedings of the 2012 ICDE Conference (ICDE’12). 1360--1369.

Digital Library

[11]

Kaushik Chakrabarti, Surajit Chaudhuri, and Venkatesh Ganti. 2011. Interval-based pruning for top-k processing over compressed lists. In Proceedings of the 2011 ICDE Conference (ICDE’11). 709--720.

Digital Library

[12]

Constantinos Dimopoulos, Sergey Nepomnyachiy, and Torsten Suel. 2013a. A candidate filtering mechanism for fast top-k query processing on modern CPUs. In Proceedings of the 2013 SIGIR Conference (SIGIR’13). 723--732.

Digital Library

[13]

Constantinos Dimopoulos, Sergey Nepomnyachiy, and Torsten Suel. 2013b. Optimizing top-k document retrieval strategies for block-max indexes. In Proceedings of the 2013 WSDM Conference (WSDM’13). 113--122.

Digital Library

[14]

Shuai Ding and Torsten Suel. 2011. Faster top-k document retrieval using block-max indexes. In Proceedings of the 2011 SIGIR Conference (SIGIR’11). 993--1002.

Digital Library

[15]

Ronald Fagin, Amnon Lotem, and Moni Naor. 2003. Optimal aggregation algorithms for middleware. Journal of Computer and System Sciences 66, 4, 614--656.

Digital Library

[16]

Ju Fan, Guoliang Li, and Lizhu Zhou. 2011. Interactive SQL query suggestion: Making databases user-friendly. In Proceedings of the 2011 ICDE Conference (ICDE’11). 351--362.

Digital Library

[17]

Ju Fan, Guoliang Li, Lizhu Zhou, Shanshan Chen, and Jun Hu. 2012. SEAL: Spatio-textual similarity search. Proceedings of the VLDB Endowment 5, 9, 824--835.

Digital Library

[18]

Marcus Fontoura, Vanja Josifovski, Jinhui Liu, Srihari Venkatesan, Xiangfei Zhu, and Jason Y. Zien. 2011. Evaluation strategies for top-k queries over memory-resident inverted indexes. Proceedings of the VLDB Endowment 4, 12, 1213--1224.

Digital Library

[19]

Zhangjie Fu, Kui Ren, Jiangang Shu, Xingming Sun, and Fengxiao Huang. 2016a. Enabling personalized search over encrypted outsourced data with efficiency improvement. IEEE Transactions on Parallel and Distributed Systems 27, 9, 2546--2559.

Digital Library

[20]

Zhangjie Fu, Xingming Sun, Sai Ji, and Guowu Xie. 2016b. Towards efficient content-aware search over encrypted outsourced data in cloud. In Proceedings of the 2016 INFOCOM Conference (INFOCOM’16). 1--9.

[21]

Zhangjie Fu, Xinle Wu, Chaowen Guan, Xingming Sun, and Kui Ren. 2016c. Toward efficient multi-keyword fuzzy search over encrypted outsourced data with accuracy improvement. IEEE Transactions on Information Forensics and Security 11, 12, 2706--2716.

[22]

Chuancong Gao and Sebastian Michel. 2012. Top-k interesting phrase mining in ad-hoc collections using sequence pattern indexing. In Proceedings of the 2012 EDBT Conference (IDBT’12). 264--275.

Digital Library

[23]

Aristides Gionis, Piotr Indyk, and Rajeev Motwani. 1999. Similarity search in high dimensions via hashing. In Proceedings of the 1999 VLDB Conference (VLDB’99). 518--529.

[24]

Ulrich Güntzer, Wolf-Tilo Balke, and Werner Kießling. 2000. Optimizing multi-feature queries for image databases. In Proceedings of the 2000 VLDB Conference (VLDB’00). 419--428.

[25]

Xin Huang, Hong Cheng, Rong-Hua Li, Lu Qin, and Jeffrey Xu Yu. 2015. Top-K structural diversity search in large networks. VLDB Journal 24, 3, 319--343.

Digital Library

[26]

Ihab F. Ilyas, George Beskales, and Mohamed A. Soliman. 2008. A survey of top-k query processing techniques in relational database systems. ACM Computing Surveys 40, 4, Article No. 11.

Digital Library

[27]

Jure Leskovec, Lada A. Adamic, and Bernardo A. Huberman. 2007. The dynamics of viral marketing. ACM Transactions on the Web 1, 1, Article No. 5.

Digital Library

[28]

Cheng Li, Yue Lu, Qiaozhu Mei, Dong Wang, and Sandeep Pandey. 2015b. Click-through prediction for advertising in Twitter timeline. In Proceedings of the 2015 KDD Conference (KDD’15).

Digital Library

[29]

Jianqiang Li, Chunchen Liu, Bo Liu, Rui Mao, Yongcai Wang, Shi Chen, Ji-Jiang Yang, Hui Pan, and Qing Wang. 2015a. Diversity-aware retrieval of medical records. Computers in Industry 69, 81--91.

Digital Library

[30]

Rong-Hua Li, Jeffrey Xu Yu, Xin Huang, Hong Cheng, and Zechao Shang. 2014. Measuring the impact of MVC attack in large complex networks. Information Sciences 278, 685--702.

[31]

Yuchen Li, Dongxiang Zhang, Ziquan Lan, and Kian-Lee Tan. 2016. Context-aware advertisement recommendation for high-speed social news feeding. In Proceedings of the 2016 ICDE Conference (ICDE’16). 505--516.

[32]

Yuchen Li, Dongxiang Zhang, and Kian-Lee Tan. 2015. Real-time targeted influence maximization for online advertisements. Proceedings of the VLDB Endowment 8, 10, 1070--1081.

Digital Library

[33]

Xiaosheng Liu, Jia Zeng, Xi Yang, Jianfeng Yan, and Qiang Yang. 2015. Scalable parallel EM algorithms for latent Dirichlet allocation in multi-core systems. In Proceedings of the 2015 WWW Conference (WWW’15). 669--679.

Digital Library

[34]

Yuanhua Lv and Ariel Fuxman. 2015. In situ insights. In Proceedings of the 2015 SIGIR Conference (SIGIR’15). 655--664.

Digital Library

[35]

Yuanhua Lv, Taesup Moon, Pranam Kolari, Zhaohui Zheng, Xuanhui Wang, and Yi Chang. 2011. Learning to model relatedness for news recommendation. In Proceedings of the 2011 WWW Conference (WWW’11). 57--66.

Digital Library

[36]

Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press, New York, NY.

[37]

Matthew Michelson and Sofus A. Macskassy. 2010. Discovering users’ topics of interest on Twitter: A first look. In Proceedings of the 2010 Workshop on Analytics for Noisy Unstructured Text Data. 73--80.

Digital Library

[38]

Alistair Moffat and Justin Zobel. 1994. Fast ranking in limited space. In Proceedings of the 1994 ICDE Conference (ICDE’94). 428--437.

[39]

Marco Pennacchiotti and Siva Gurumurthy. 2011. Investigating topic models for social media user recommendation. In Proceedings of the 2011 WWW Conference (WWW’11). 101--102.

Digital Library

[40]

Michael Persin. 1994. Document filtering for fast ranking. In Proceedings of the 1994 SIGIR Conference (SIGIR’94). 339--348.

[41]

Daniel Ramage, Susan T. Dumais, and Daniel J. Liebling. 2010. Characterizing microblogs with topic models. In Proceedings of the 2010 ICWSM Conference (ICWSM’10).

[42]

Stephen E. Robertson, Steve Walker, Susan Jones, Micheline Hancock-Beaulieu, and Mike Gatford. 1994. Okapi at TREC-3. In Proceedings of the 1994 TREC Conference (TREC’94). 109--126.

[43]

Cristian Rossi, Edleno Silva de Moura, Andre L. Carvalho, and Altigran Soares da Silva. 2013. Fast document-at-a-time query processing using two-tier indexes. In Proceedings of the 2013 SIGIR Conference (SIGIR’13). 183--192.

Digital Library

[44]

Trevor Strohman and W. Bruce Croft. 2007. Efficient document retrieval in main memory. In Proceedings of the 2007 SIGIR Conference (SIGIR’07). 175--182.

Digital Library

[45]

Howard R. Turtle and James Flood. 1995. Query evaluation: Strategies and optimizations. Information Processing and Management 31, 6, 831--850.

Digital Library

[46]

Xing Wei and W. Bruce Croft. 2006. LDA-based document models for ad-hoc retrieval. In Proceedings of the 2006 SIGIR Conference (SIGIR’06). 178--185.

Digital Library

[47]

Linkai Weng, Zhiwei Li, Rui Cai, Yaoxue Zhang, Yuezhi Zhou, Laurence Tianruo Yang, and Lei Zhang. 2011. Query by document via a decomposition-based two-level retrieval approach. In Proceedings of the 2011 SIGIR Conference (SIGIR’11). 505--514.

Digital Library

[48]

Zhihua Xia, Xinhui Wang, Xingming Sun, and Qian Wang. 2016. A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Transactions on Parallel and Distributed Systems 27, 2, 340--352.

Digital Library

[49]

Yin Yang, Nilesh Bansal, Wisam Dakka, Panagiotis G. Ipeirotis, Nick Koudas, and Dimitris Papadias. 2009. Query by document. In Proceedings of the 2009 WSDM Conference (WSDM’09). 34--43.

Digital Library

[50]

Xing Yi and James Allan. 2009. A comparative study of utilizing topic models for information retrieval. In Proceedings of the 2009 ECIR Conference (ICIR’09). 29--41.

Digital Library

[51]

Dongxiang Zhang, Chee-Yong Chan, and Kian-Lee Tan. 2014a. An efficient publish/subscribe index for ecommerce databases. Proceedings of the VLDB Endowment 7, 8, 613--624.

Digital Library

[52]

Dongxiang Zhang, Chee-Yong Chan, and Kian-Lee Tan. 2014b. Processing spatial keyword query as a top-k aggregation query. In Proceedings of the 2014 SIGIR Conference (SIGIR’14). 355--364.

Digital Library

[53]

Dongxiang Zhang, Beng Chin Ooi, and Anthony K. H. Tung. 2010. Locating mapped resources in Web 2.0. In Proceedings of the 2010 ICDE Conference (ICDE’10). 521--532.

Cited By

Wang YLi YTan K(2019)Efficient Representative Subset Selection over Sliding WindowsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.285418231:7(1327-1340)Online publication date: 1-Jul-2019
https://doi.org/10.1109/TKDE.2018.2854182
Li QWu QZhu CZhang J(2019)Bi-Level Masked Multi-scale CNN-RNN Networks for Short Text Representation2019 International Conference on Document Analysis and Recognition (ICDAR)10.1109/ICDAR.2019.00147(888-893)Online publication date: Sep-2019
https://doi.org/10.1109/ICDAR.2019.00147
Liu ZYang YHuang ZShen FZhang DShen H(2019)Embedding and predicting the event at early stageWorld Wide Web10.1007/s11280-018-0545-622:3(1055-1074)Online publication date: 21-May-2019
https://dl.acm.org/doi/10.1007/s11280-018-0545-6
Show More Cited By

Index Terms

Processing Long Queries Against Short Text: Top-k Advertisement Matching in News Stream Applications
1. Information systems
  1. Information retrieval
    1. Search engine architectures and scalability
      1. Search engine indexing

Recommendations

Information Retrieval with Verbose Queries
SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Recently, the focus of many novel search applications shifted from short keyword queries to verbose natural language queries. Examples include question answering systems and dialogue systems, voice search on mobile devices and entity search engines like ...
Reducing long queries using query quality predictors
SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

Long queries frequently contain many extraneous terms that hinder retrieval of relevant documents. We present techniques to reduce long queries to more effective shorter ones that lack those extraneous terms. Our work is motivated by the observation ...
Mining Historic Query Trails to Label Long and Rare Search Engine Queries

Web search engines can perform poorly for long queries (i.e., those containing four or more terms), in part because of their high level of query specificity. The automatic assignment of labels to long queries can capture aspects of a user’s search ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 35, Issue 3

July 2017

410 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3026478

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

Issue’s Table of Contents

Copyright © 2017 ACM.

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 May 2017

Accepted: 01 December 2016

Revised: 01 November 2016

Received: 01 April 2016

Published in TOIS Volume 35, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Nature Science Foundation of China
CCF-Tencent Open Research Fund
Start-Up Research Grant of Renmin University of China
Fundamental Research Funds for the Central Universities
Priority Academic Program Development of Jiangsu Higher Education Institutions
Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
327
Total Downloads

Downloads (Last 12 months)18
Downloads (Last 6 weeks)1

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang YLi YTan K(2019)Efficient Representative Subset Selection over Sliding WindowsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.285418231:7(1327-1340)Online publication date: 1-Jul-2019
https://doi.org/10.1109/TKDE.2018.2854182
Li QWu QZhu CZhang J(2019)Bi-Level Masked Multi-scale CNN-RNN Networks for Short Text Representation2019 International Conference on Document Analysis and Recognition (ICDAR)10.1109/ICDAR.2019.00147(888-893)Online publication date: Sep-2019
https://doi.org/10.1109/ICDAR.2019.00147
Liu ZYang YHuang ZShen FZhang DShen H(2019)Embedding and predicting the event at early stageWorld Wide Web10.1007/s11280-018-0545-622:3(1055-1074)Online publication date: 21-May-2019
https://dl.acm.org/doi/10.1007/s11280-018-0545-6
Wang YLi YFan JTan K(2018)Location-aware Influence Maximization over Dynamic Social StreamsACM Transactions on Information Systems10.1145/323087136:4(1-35)Online publication date: 27-Jul-2018
https://dl.acm.org/doi/10.1145/3230871
Zhang PBao ZLi YLi GZhang YPeng ZGuo YFarooq F(2018)Trajectory-driven Influential Billboard PlacementProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3219946(2748-2757)Online publication date: 19-Jul-2018
https://dl.acm.org/doi/10.1145/3219819.3219946
Sha MLi YWang YGuo WTan K(2018)River: A Real-Time Influence Monitoring System on Social Media Streams2018 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2018.00203(1429-1434)Online publication date: Nov-2018
https://doi.org/10.1109/ICDMW.2018.00203
Wang YLi YTan K(2018)A Sliding-Window Framework for Representative Subset Selection2018 IEEE 34th International Conference on Data Engineering (ICDE)10.1109/ICDE.2018.00127(1268-1271)Online publication date: Apr-2018
https://doi.org/10.1109/ICDE.2018.00127
Chen YZhang HLiu RYe ZLin J(2018)Experimental explorations on short text topic mining between LDA and NMF based SchemesKnowledge-Based Systems10.1016/j.knosys.2018.08.011Online publication date: Aug-2018
https://doi.org/10.1016/j.knosys.2018.08.011
Yang DGuo JWang ZWang YZhang JHu LYin JCao J(2018)FastPM: An approach to pattern matching via distributed stream processingInformation Sciences10.1016/j.ins.2018.04.031453(263-280)Online publication date: Jul-2018
https://doi.org/10.1016/j.ins.2018.04.031
Wang YJiang XLee LChew ETan K(2017)Tree based searching approaches for integrated vehicle dispatching and container allocation in a transshipment hubExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.01.00374:C(139-150)Online publication date: 15-May-2017
https://dl.acm.org/doi/10.1016/j.eswa.2017.01.003

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents