research-article

Discovering and using groups to improve personalized search

Authors:

Meredith Ringel Morris,

Steve BushAuthors Info & Claims

WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining

Pages 15 - 24

https://doi.org/10.1145/1498759.1498786

Published: 09 February 2009 Publication History

Abstract

Personalized Web search takes advantage of information about an individual to identify the most relevant results for that person. A challenge for personalization lies in collecting user profiles that are rich enough to do this successfully. One way an individual's profile can be augmented is by using data from other people. To better understand whether groups of people can be used to benefit personalized search, we explore the similarity of query selection, desktop information, and explicit relevance judgments across people grouped in different ways. The groupings we explore fall along two dimensions: the longevity of the group members' relationship, and how explicitly the group is formed. We find that some groupings provide valuable insight into what members consider relevant to queries related to the group focus, but that it can be difficult to identify valuable groups implicitly. Building on these findings, we explore an algorithm to "groupize" (versus "personalize") Web search results that leads to a significant improvement in result ranking on group-relevant queries.

References

[1]

Almeida, R. and Almeida, V. (2004). A community-aware search engine. In Proc. of WWW '04, 413--421.

Digital Library

[2]

Amershi, S. and Morris, M. R. (2008). CoSearch: A system for co-located collaborative Web search. In Proc. of CHI '08.

Digital Library

[3]

Belkin, N. J., Cool, C., Croft, W. B., and Callan, J. P. (1993). The effect of multiple query representations on information retrieval system performance. In Proc. Of SIGIR '93, 339--346.

Digital Library

[4]

Dou, Z., Song, R., and Wen, J. R. (2007). A large-scale evaluation and analysis of personalized search strategies. In Proc. of WWW '07, 581--590.

Digital Library

[5]

Freyne, J. and Smyth, B. (2006). Cooperating search communities. In Proc. of AH '06, 101--110.

Digital Library

[6]

Järvelin, K. and Kekäläinen, J. (2000). IR evaluation methods for retrieving highly relevant documents. In Proc. of SIGIR '00.

Digital Library

[7]

Jones, R., Kumar, R., Pang, B., and Tomkins, A. (2007). "I know what you did last summer": Query logs and user privacy. In Proc. CIKM '07, 909--914.

Digital Library

[8]

Lee, Y-J. (2005). VizSearch: A collaborative Web searching environment. Computers & Education, 44(4): 423--439.

Digital Library

[9]

Mei, Q. and Church, K. (2008). Entropy of search logs: How hard is search? With personalization? With backoff? In Proc. of WSDM '08.

Digital Library

[10]

Morris, M. R., Teevan, J., and Bush, S. (2008). Enhancing collaborative Web search with personalization: Groupization, smart splitting, and group hit-highlighting. Proc. of CSCW '08.

Digital Library

[11]

Morris, M. R. (2008). A survey of collaborative Web search practices. In Proc. of CHI '08, 1657--1660.

Digital Library

[12]

Morris, M. R. and Horvitz, E. (2007). SearchTogether: An interface for collaborative Web search. In Proc. of UIST '07.

Digital Library

[13]

O'Conner, M., Cosley, D., Konstan, J., and Riedl, J. (2001). PolyLens: A recommender system for groups of users. In Proc. of ESCW '01, 199--218.

Digital Library

[14]

Pickens, J., Golovchinsky, G., Shah, C., Qvarfordt, P., Back, M. (2008). Algorithmic mediation for collaborative exploratory search. In Proc. of SIGIR 2008.

Digital Library

[15]

Pitkow, J., Schutze, H., Cass, T., Cooley, R., Turnbull, D., Edmonds, A., Adar, E., and Breuel, T. (2002). Personalized search. Communications of the ACM, 45(9): 50--55.

Digital Library

[16]

Shen, X., Tan, B., and Zhai, C. X. (2005). Implicit user modeling for personalized search. Proc. of CIKM '05, 824--831.

Digital Library

[17]

Smyth, B. (2007). A community-based approach to personalizing Web search. IEEE Computer, 40(8): 42--50.

Digital Library

[18]

Sparck Jones, K., Walker, S., and Robertson, S. A. (1998). Probabilistic model of information retrieval: Development and status. TR-446, Cambridge University Computer Laboratory.

[19]

Spink, A. and Jansen, B. (2004). Web Search: Public Searching of the Web. Kluwer Academic Publishers.

Digital Library

[20]

Sugiyama, K., Hatano, K., and Yoshikawa, M. (2004). Adaptive Web search based on user profile constructed without any effort from users. In Proc. of WWW '04, 675--684.

Digital Library

[21]

Sun, J.-T., Zeng, H.-J., Liu, H., Lu, Y., and Chen, Z. (2005). CubeSVD: A novel approach to personalized Web search. In Proc. of WWW '05, 382--390.

Digital Library

[22]

Teevan, J., Dumais, S. T., and Horvitz, E. (2005). Personalizing search via automated analysis of interests and activities. In Proc. of SIGIR '05, 449--456.

Digital Library

[23]

Teevan, J., Dumais, S. T., and Horvitz, E. (2005). Beyond the commons: On the value of personalizing Web search. In Proc. of PIA '05 Workshop, 84--92.

[24]

Twidale, M., Nichols, D. and Paice, C. (1997). Browsing is a collaborative process. IP&M, 33(6): 761--783.

Digital Library

[25]

Voorhees, E. and Harman, D. (Eds.) (2005). TREC: Experimental Evaluation of Information Retrieval. MIT Press.

Cited By

White R(2024)Advancing the Search Frontier with AI AgentsCommunications of the ACM10.1145/3655615Online publication date: 20-Aug-2024
https://doi.org/10.1145/3655615
Meng YLi RQin HWu XDuan HLu YWang G(2024)Encoding Group Interests With Persistent Homology for Personalized SearchIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.341002954:9(5606-5616)Online publication date: Sep-2024
https://doi.org/10.1109/TSMC.2024.3410029
Skjæveland MBalog KBernard NŁajewska WLinjordet T(2024)An ecosystem for personal knowledge graphs: A survey and research roadmapAI Open10.1016/j.aiopen.2024.01.0035(55-69)Online publication date: 2024
https://doi.org/10.1016/j.aiopen.2024.01.003
Show More Cited By

Index Terms

Discovering and using groups to improve personalized search

Recommendations

Integrating collaborative filtering and matching-based search for product recommendations

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good ...
Understanding collaborative filtering parameters for personalized recommendations in e-commerce

Collaborative Filtering (CF) is a popular method for personalizing product recommendations for e-Commerce and customer relationship management (CRM). CF utilizes the explicit or implicit product evaluation ratings of customers to develop personalized ...
Integrating Collaborative Filtering and Search-Based Techniques for Personalized Online Product Recommendation
ICDMW '11: Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops

The existing Collaborative Filtering (CF) technique that has been widely applied by e-commerce sites requires a large amount of ratings data to make meaningful recommendations. It is not directly applicable for recommending products that are not ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining

February 2009

314 pages

ISBN:9781605583907

DOI:10.1145/1498759

Editors:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Paolo Boldi
Universita degli Studi di Milano, Italy
,
Berthier Ribeiro-Neto
Google Engineering, Brazil & CS Dept., Univ. Fed. de Minas Gerais, Brazil
,
B. Barla Cambazoglu
Yahoo! Research

Copyright © 2009 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]

Sponsors

SIGMOD: ACM Special Interest Group on Management of Data
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
Yahoo! Research
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
Nokia
Google Inc.
SIGIR: ACM Special Interest Group on Information Retrieval
Microsoft: Microsoft

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 February 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WSDM'09

Sponsor:

WSDM'09: Second ACM International Conference on Web Search and Web Data Mining

February 9 - 12, 2009

Barcelona, Spain

Acceptance Rates

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

91
Total Citations
View Citations
1,240
Total Downloads

Downloads (Last 12 months)23
Downloads (Last 6 weeks)3

Reflects downloads up to 12 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

White R(2024)Advancing the Search Frontier with AI AgentsCommunications of the ACM10.1145/3655615Online publication date: 20-Aug-2024
https://doi.org/10.1145/3655615
Meng YLi RQin HWu XDuan HLu YWang G(2024)Encoding Group Interests With Persistent Homology for Personalized SearchIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.341002954:9(5606-5616)Online publication date: Sep-2024
https://doi.org/10.1109/TSMC.2024.3410029
Skjæveland MBalog KBernard NŁajewska WLinjordet T(2024)An ecosystem for personal knowledge graphs: A survey and research roadmapAI Open10.1016/j.aiopen.2024.01.0035(55-69)Online publication date: 2024
https://doi.org/10.1016/j.aiopen.2024.01.003
Mahrík MŠikyňa MMic VZezula P(2024)Towards Personalized Similarity Search for Vector DatabasesSimilarity Search and Applications10.1007/978-3-031-75823-2_11(126-139)Online publication date: 25-Oct-2024
https://doi.org/10.1007/978-3-031-75823-2_11
Ye YLi ZDou ZZhu YZhang CWu SCao ZWilliams BChen YNeville J(2023)Learning from the wisdom of crowdsProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i4.25607(4818-4826)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i4.25607
Sangamithra BManjunath Swamy BSunil Kumar M(2022)Personalized Ranking Mechanism Using Yandex Dataset on Machine Learning ApproachesProceedings of the International Conference on Cognitive and Intelligent Computing10.1007/978-981-19-2350-0_61(629-639)Online publication date: 1-Nov-2022
https://doi.org/10.1007/978-981-19-2350-0_61
Neumayr TAugstein M(2020)A Systematic Review of Personalized Collaborative SystemsFrontiers in Computer Science10.3389/fcomp.2020.5626792Online publication date: 26-Nov-2020
https://doi.org/10.3389/fcomp.2020.562679
Liu JLiu CBelkin N(2020)Personalization in text information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2423471:3(349-369)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24234
Nguyen DNguyen DNguyen TPhung DGromann DEspinosa Anke LDeclerck T(2019)A convolutional neural network-based model for knowledge base completion and its application to search personalizationSemantic Web10.3233/SW-18031810:5(947-960)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/SW-180318
Jiang DChen H(2019)Cohort-based personalized query auto-completionFrontiers of Information Technology & Electronic Engineering10.1631/FITEE.180001020:9(1246-1258)Online publication date: 18-Oct-2019
https://doi.org/10.1631/FITEE.1800010
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.

Media

Figures

Other

Tables

View Table of Contents