K. Sugiyama, K. Hatano, and M. Yoshikawa. Adaptive web search based on user profile constructed without any effort from users. In Proceedings of WWW 2004, 2004.

Digital Library

Google Scholar

[6]

R. W. White, J. M. Jose, C. J. van Rijsbergen, and I. Ruthven. A simulated study of implicit feedback models. In Proceedings of ECIR 2004, pages 311--326, 2004.

Crossref

Google Scholar

Cited By

View all

Pàmies-Estrems DGarcia-Alfaro J(2023)On the self-adjustment of privacy safeguards for query log streamsComputers & Security10.1016/j.cose.2023.103450134(103450)Online publication date: Nov-2023
https://doi.org/10.1016/j.cose.2023.103450
Kim KJeong SJung YLee S(2015)Category-Based an Intelligent Search System Using a Variety of Recommendation DataComputer Science and its Applications10.1007/978-3-662-45402-2_92(655-660)Online publication date: 2015
https://doi.org/10.1007/978-3-662-45402-2_92
Bennett PRadlinski FWhite RYilmaz EMa WNie JBaeza-Yates RChua TCroft W(2011)Inferring and using location metadata to personalize web searchProceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval10.1145/2009916.2009938(135-144)Online publication date: 24-Jul-2011
https://dl.acm.org/doi/10.1145/2009916.2009938
Show More Cited By

Index Terms

UCAIR: a personalized search toolbar
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment

Recommendations

Generalized Collaborative Personalized Ranking for Recommendation
Web and Big Data
Abstract
Data sparsity is a common problem in collaborative ranking for personalized recommendation with implicit feedback. Several previous work tried to ‘borrow’ feedback information from users’ neighborhood as their prior preferences to alleviate this ...
Using Implicit Feedback for Neighbors Selection: Alleviating the Sparsity Problem in Collaborative Recommendation Systems
WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web

The most popular Recommender systems (RSs) employ Collaborative Filtering (CF) algorithms where users explicitly rate items. Based on these ratings, a user-item rating matrix is generated and used to select the items to be recommended for a target user. ...
Building recommender systems for scholarly information
SWM '17: Proceedings of the 1st Workshop on Scholarly Web Mining

The depth and breadth of research now being published is overwhelming for an individual researcher to keep track of let alone consume. Recommender systems have been developed to make it easier for researchers to discover relevant content. However, these ...

Comments

Information & Contributors

Information

Published In

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

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: 15 August 2005

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

SIGIR05

Sponsor:

SIGIR

SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
521
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)1

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Pàmies-Estrems DGarcia-Alfaro J(2023)On the self-adjustment of privacy safeguards for query log streamsComputers & Security10.1016/j.cose.2023.103450134(103450)Online publication date: Nov-2023
https://doi.org/10.1016/j.cose.2023.103450
Kim KJeong SJung YLee S(2015)Category-Based an Intelligent Search System Using a Variety of Recommendation DataComputer Science and its Applications10.1007/978-3-662-45402-2_92(655-660)Online publication date: 2015
https://doi.org/10.1007/978-3-662-45402-2_92
Bennett PRadlinski FWhite RYilmaz EMa WNie JBaeza-Yates RChua TCroft W(2011)Inferring and using location metadata to personalize web searchProceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval10.1145/2009916.2009938(135-144)Online publication date: 24-Jul-2011
https://dl.acm.org/doi/10.1145/2009916.2009938
Gou LZhang XChen HKim JGiles CHunter JLagoze CGiles LLi Y(2010)Social network document rankingProceedings of the 10th annual joint conference on Digital libraries10.1145/1816123.1816170(313-322)Online publication date: 21-Jun-2010
https://dl.acm.org/doi/10.1145/1816123.1816170
Reformat MGolmohammadi S(2009)Updating user profile using ontology-based semantic similarityProceedings of the 18th international conference on Fuzzy Systems10.5555/1717561.1717745(1062-1067)Online publication date: 20-Aug-2009
https://dl.acm.org/doi/10.5555/1717561.1717745
Mei QChurch KNajork MBroder AChakrabarti S(2008)Entropy of search logsProceedings of the 2008 International Conference on Web Search and Data Mining10.1145/1341531.1341540(45-54)Online publication date: 11-Feb-2008
https://dl.acm.org/doi/10.1145/1341531.1341540
Baishya D(2007)Enhanced visual experience and archival reusability in personalized search based on modified spider graphProceedings of the 3rd international conference on Advances in visual computing - Volume Part II10.5555/1779090.1779168(721-731)Online publication date: 26-Nov-2007
https://dl.acm.org/doi/10.5555/1779090.1779168
Sieg AMobasher BBurke R(2007)Representing context in web search with ontological user profilesProceedings of the 6th international and interdisciplinary conference on Modeling and using context10.5555/1770806.1770839(439-452)Online publication date: 20-Aug-2007
https://dl.acm.org/doi/10.5555/1770806.1770839
Sieg AMobasher BBurke R(2007)Representing Context in Web Search with Ontological User ProfilesModeling and Using Context10.1007/978-3-540-74255-5_33(439-452)Online publication date: 2007
https://doi.org/10.1007/978-3-540-74255-5_33
Tan BShen XZhai CEliassi-Rad TUngar LCraven MGunopulos D(2006)Mining long-term search history to improve search accuracyProceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/1150402.1150493(718-723)Online publication date: 20-Aug-2006
https://dl.acm.org/doi/10.1145/1150402.1150493

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.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Generalized Collaborative Personalized Ranking for Recommendation

Using Implicit Feedback for Neighbors Selection: Alleviating the Sparsity Problem in Collaborative Recommendation Systems

Building recommender systems for scholarly information

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations