research-article

From devices to people: attribution of search activity in multi-user settings

Authors:

Eric HorvitzAuthors Info & Claims

WWW '14: Proceedings of the 23rd international conference on World wide web

Pages 431 - 442

https://doi.org/10.1145/2566486.2568022

Published: 07 April 2014 Publication History

Abstract

Online services rely on unique identifiers of machines to tailor offerings to their users. An implicit assumption is made that each machine identifier maps to an individual. However, shared ma-chines are common, leading to interwoven search histories and noisy signals for applications such as personalized search and ad-vertising. We present methods for attributing search activity to individual searchers. Using ground truth data for a sample of almost four million U.S. Web searchers-containing both machine identifiers and person identifiers-we show that over half of the machine identifiers comprise the queries of multiple people. We characterize variations in features of topic, time, and other aspects such as the complexity of the information sought per the number of searchers on a machine, and show significant differences in all measures. Based on these insights, we develop models to accurately estimate when multiple people contribute to the logs ascribed to a single machine identifier. We also develop models to cluster search behavior on a machine, allowing us to attribute historical data accurately and automatically assign new search activity to the correct searcher. The findings have implications for the design of applications such as personalized search and advertising that rely heavily on machine identifiers to custom-tailor their services.

References

[1]

Amari, S.I., Cichocki, A., and Yang, H.H. (1996). A new learning algorithm for blind signal separation. Proc. NIPS, 757--763.

[2]

Allen, B. (2000). Individual differences and conundrums of user-centered design. JASIS, 51(6): 508--520.

Digital Library

[3]

Anand, K., Mathew, G., and Reddy, V. (1995). Blind separation of multiple co-channel BPSK signals arriving at an anten-na array. IEEE Signal Processing Letters, 2: 176--178.

[4]

Barabasi, A.L. (2005). The origin of bursts and heavy tails in human dynamics. Nature, 435(7039): 207--211.

[5]

Bennett, P.N., Svore, K., and Dumais, S.T. (2010). Classification-enhanced ranking. Proc. WWW, 111--120.

Digital Library

[6]

Bhavnani, S. (2001). Important cognitive components of domain-specific search knowledge. Proc. TREC, 571--578.

[7]

Bilenko, M. and Richardson, M. (2011). Predictive client-side profiles for personalized advertising. Proc. SIGKDD, 413--421.

Digital Library

[8]

Buscher, G., White, R.W., Dumais, S.T., and Huang, J. (2012). Large-scale analysis of individual and task differences on search result page examination strategies. Proc. WSDM, 373--424.

Digital Library

[9]

Cadez, I., Heckerman, D., Meek, C., Smyth, P., and White, S. (2003). Visualization of navigation patterns on a web site using model based clustering. Data Mining and Knowledge Dis-covery, 7: 399--424.

Digital Library

[10]

Cardoso, J.F. (1998). Blind signal separation: statistical prin-ciples. Proc. IEEE, 86(10): 2009--2025.

[11]

Chen, Y., Pavlov, D., and Canny, J. (2009). Large-scale be-havioral targeting. Proc. SIGKDD, 209--218.

Digital Library

[12]

Collins-Thompson, K., and Callan, J. (2004). A language modeling approach to predicting reading difficulty. Proc. HLT, 193--200.

[13]

Comon, P. (1994). Independent component analysis: a new concept? Signal Processing, 36(3): 287--314.

Digital Library

[14]

Dasgupta, A., Gurevich, M., Zhang, L., Tseng, B., and Thom-as, A.O. (2012). Overcoming browser cookie churn with clus-tering. Proc. WSDM, 83--92.

Digital Library

[15]

Dou, Z., Song, R., and Wen, J.R. (2007). A large-scale evaluation and analysis of personalized search strategies. Proc. WWW, 581--590.

Digital Library

[16]

Downey, D., Dumais, S.T., and Horvitz, E. (2007). Models of searching and browsing: languages, studies, and applications. Proc. IJCAI, 2740--2747

Digital Library

[17]

Dumais, S., Buscher, G., and Cutrell, E. (2010). Individual differences in gaze patterns for Web search. Proc. IIiX, 185--194.

Digital Library

[18]

Dupret, G. and Piwowarski, B. (2008). A user browsing model to predict search engine click data from past observations. Proc. SIGIR, 331--338.

Digital Library

[19]

Fawcett, T. and Provost, F. (1997). Adaptive fraud detection. Data Mining and Knowledge Discovery, 1(3): 291--316.

Digital Library

[20]

File, T. (2013) Computer and Internet Use in the United States. http://www.census.gov/prod/2013pubs/p20--569.pdf

[21]

Fulgoni, G.M. (2005). The "Professional Respondent" Problem in Online Survey Panels Today. Slides online at: http://www.sigmavalidation.com/tips/05_06_02_Online_Survey_Panels.ppt (Downloaded on October 3, 2013).

[22]

Hu, V, Stone, M., Pedersen, J., and White, R.W. (2011). Effects of search success on search engine re-use. Proc. CIKM, 1841--1846.

Digital Library

[23]

Friedman, J.H., Hastie, T., and Tibshirani, R. (1998). Additive logistic regression: A statistical view of boosting. Technical Report, Department of Statistics, Stanford University.

[24]

Joachims, T. (2002). Optimizing search engines using click-through data. Proc. SIGKDD, 133--142.

Digital Library

[25]

Kobsa, A. (2007). Privacy-enhanced personalization. CACM, 50(8): 24--33.

Digital Library

[26]

Krause, A. and Horvitz, E. (2008). A utility-theoretic approach to privacy and personalization. Proc. AAAI, 1181--1188.

Digital Library

[27]

Lau, T. and Horvitz, E. (1999). Patterns of search: analyzing and modeling web query refinement. Proc. UM, 119--128.

Digital Library

[28]

MacQueen, J.B. (1967). Some methods for classification and analysis of multivariate observations. Proc. Symposium on Math, Statistics, and Probability, 281--297.

[29]

Matthijs, N. and Radlinski, F. (2011). Personalizing web search using long term browsing history. Proc. WSDM, 25--34.

Digital Library

[30]

Richardson, M. (2009). Learning about the world from long-term query logs. ACM TWEB, 2(4): 21.

Digital Library

[31]

Saracevic, T. (1991). Individual differences in organizing, searching and retrieving information. Proc. ASIS, 82--86.

[32]

Shen, X., Dumais, S., and Horvitz, E. (2005). Analysis of topic dynamics in web search. Proc. WWW, 1102--1103.

Digital Library

[33]

Shen, X., Tan, B., and Zhai, C.X. (2005). Implicit user modeling for personalized search. Proc. CIKM, 824--831

Digital Library

[34]

Sontag, D., Collins-Thompson, K., Bennett, P.N., White, R.W., Dumais, S., and Billerbeck, B. (2012). Probabilistic models for personalizing web search. Proc. WSDM, 433--442.

Digital Library

[35]

Tan, B., Shen, X., and Zhai, C. (2006). Mining long-term search history to improve search accuracy. Proc. SIGKDD, 718--723.

Digital Library

[36]

Teevan, J., Dumais, S. T., and Horvitz, E. (2005). Personalizing search via automated analysis of interests and activities. Proc. SIGIR, 449--456.

Digital Library

[37]

Teevan, J., Liebling, D.J., and Geetha, G.R. (2011). Understanding and predicting personal navigation. Proc. WSDM, 85--94.

Digital Library

[38]

Thatcher, A. (2008). Web search strategies: The influence of web experience and task type. IP&M, 44(3): 1308--1329.

Digital Library

[39]

White, R.W. and Morris, D. (2007). Investigating the querying and browsing behavior of advanced search engine users. Proc. SIGIR, 255--262.

Digital Library

[40]

White, R.W. and Drucker, S. (2007). Investigating behavioral variability in web search. Proc. WWW, 21--30.

Digital Library

[41]

White, R.W., Bailey, P., and Chen, L. (2009). Predicting user interests from contextual information. Proc. SIGIR, 363--370.

Digital Library

[42]

White, R.W., Dumais, S.T., and Teevan, J. (2009). Character-izing the influence of domains expertise on web search behavior. Proc. WSDM, 132--141.

Digital Library

[43]

White, R.W., Bennett, P.N., and Dumais, S.T. (2010). Predicting short-term interests using activity-based search context. Proc. CIKM, 1009--1018.

Digital Library

[44]

Xiang, B., Jiang, D., Pei, J., Sun, X., Chen, E., and Li, H. (2010). Context-aware ranking in web search. Proc. SIGIR. 451--458.

Digital Library

[45]

Yan, J., Liu, N., Wang, G., Zhang, W., Jiang, Y., and Chen, Z. (2009). How much can behavioral targeting help online advertising? Proc. WWW, 261--270.

Digital Library

[46]

Zhao Y. and Karypis, G. (2002). Criterion functions for document clustering: Experiments and analysis. Proc. CIKM, 515--524.

Cited By

Gao CWang MChen J(2023)Item Recommendation on Shared Accounts Through User IdentificationSocial Media Processing10.1007/978-981-99-7596-9_5(63-76)Online publication date: 15-Nov-2023
https://doi.org/10.1007/978-981-99-7596-9_5
Buhalis DVolchek K(2022)Bridging marketing theory and big data analyticsInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2020.10225356:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.ijinfomgt.2020.102253
Ghosh ATschiatschek SMahdavi HSingla AEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Towards Deployment of Robust Cooperative AI AgentsProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398817(447-455)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3398817
Show More Cited By

Index Terms

From devices to people: attribution of search activity in multi-user settings
1. Information systems
  1. Information retrieval

Recommendations

Enhancing personalization via search activity attribution
SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Online services rely on machine identifiers to tailor services such as personalized search and advertising to individual users. The assumption made is that each identifier comprises the behavior of a single person. However, shared machine usage is ...
Multilingual people search
SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

People Search is an important search service with multiple applications (eg. looking up a friend on Facebook, finding colleagues in corporate email directories etc). With the proportion of non-English users on a steady rise, people search services are ...
Indigenous people on the web

This paper explores the ways in which Indigenous people around the world are participating in the World Wide Web, through establishing their own websites or accessing services via the Web. Indigenous websites are remarkably diverse: in addition to those ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '14: Proceedings of the 23rd international conference on World wide web

April 2014

926 pages

ISBN:9781450327442

DOI:10.1145/2566486

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Copyright © 2014 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

Acceptance Rates

WWW '14 Paper Acceptance Rate 84 of 645 submissions, 13%;

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
279
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gao CWang MChen J(2023)Item Recommendation on Shared Accounts Through User IdentificationSocial Media Processing10.1007/978-981-99-7596-9_5(63-76)Online publication date: 15-Nov-2023
https://doi.org/10.1007/978-981-99-7596-9_5
Buhalis DVolchek K(2022)Bridging marketing theory and big data analyticsInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2020.10225356:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.ijinfomgt.2020.102253
Ghosh ATschiatschek SMahdavi HSingla AEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Towards Deployment of Robust Cooperative AI AgentsProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398817(447-455)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3398817
Wei CGu QJi SChen WWang ZBeyah R(2020)OB-WSPES: A Uniform Evaluation System for Obfuscation-based Web Search PrivacyIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2019.2962440(1-1)Online publication date: 2020
https://doi.org/10.1109/TDSC.2019.2962440
Jiang JLi CChen YWang WCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Identifying Users behind Shared Accounts in Online Streaming ServicesThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210054(65-74)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210054
Tang S(2017)Robust advertisement allocationProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3171837.3171904(4419-4425)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3171837.3171904
Rieh SCollins-Thompson KHansen PLee H(2016)Towards searching as a learning processJournal of Information Science10.1177/016555151561584142:1(19-34)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1177/0165551515615841
Wang LLee KLu QMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Improving Advertisement Recommendation by Enriching User Browser Cookie AttributesProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983374(2401-2404)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983374
Bindschaedler VShokri R(2016)Synthesizing Plausible Privacy-Preserving Location Traces2016 IEEE Symposium on Security and Privacy (SP)10.1109/SP.2016.39(546-563)Online publication date: May-2016
https://doi.org/10.1109/SP.2016.39
Aharon MHillel EKagian ALempel RMakabee HNissim R(2015)Watch-it-nextProceedings of the 2015th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part III10.5555/3120539.3120552(180-195)Online publication date: 7-Sep-2015
https://dl.acm.org/doi/10.5555/3120539.3120552
Show More Cited By

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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten