Personalized ranking for digital libraries based on log analysis

Yang Sun, Huajing Li, Isaac G. Councill, Jian Huang, Wang Chien Lee, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

Given the exponential increase of indexable context on the Web, ranking is an increasingly difficult problem in information retrieval systems. Recent research shows that implicit feedback regarding user preferences can be extracted from web access logs in order to increase ranking performance. We analyze the implicit user feedback from access logs in the CiteSeer academic search engine and show how site structure can better inform the analysis of clickthrough feedback providing accurate personalized ranking services tailored to individual information retrieval systems. Experiment and analysis shows that our proposed method is more accurate on predicting user preferences than any non-personalized ranking methods when user preferences are stable over time. We compare our method with several non-personalized ranking methods including ranking SVMlight as well as several ranking functions specific to the academic document domain. The results show that our ranking algorithm can reach 63.59% accuracy in comparison to 50.02% for ranking SVMlight and below 43% for all other single feature ranking methods. We also show how the derived personalized ranking vectors can be employed for other ranking-related purposes such as recommendation systems.

Original languageEnglish (US)
Title of host publicationProceedings of the 10th ACM Workshop on Web Information and Data Management, WIDM '08, Co-located with the ACM 17th Conference on Information and Knowledge Management, CIKM '08
Pages133-140
Number of pages8
DOIs
StatePublished - Dec 1 2008
Event10th ACM Workshop on Web Information and Data Management, WIDM '08, Co-located with the ACM 17th Conference on Information and Knowledge Management, CIKM '08 - Napa Valley, CA, United States
Duration: Oct 26 2008Oct 30 2008

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other10th ACM Workshop on Web Information and Data Management, WIDM '08, Co-located with the ACM 17th Conference on Information and Knowledge Management, CIKM '08
CountryUnited States
CityNapa Valley, CA
Period10/26/0810/30/08

Fingerprint

Ranking
Digital libraries
User preferences
World Wide Web
Information retrieval
Recommendation system
Clickthrough
Ranking function
Experiment
Search engine
Implicit feedback

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

Cite this

Sun, Y., Li, H., Councill, I. G., Huang, J., Lee, W. C., & Giles, C. L. (2008). Personalized ranking for digital libraries based on log analysis. In Proceedings of the 10th ACM Workshop on Web Information and Data Management, WIDM '08, Co-located with the ACM 17th Conference on Information and Knowledge Management, CIKM '08 (pp. 133-140). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/1458502.1458524
Sun, Yang ; Li, Huajing ; Councill, Isaac G. ; Huang, Jian ; Lee, Wang Chien ; Giles, C. Lee. / Personalized ranking for digital libraries based on log analysis. Proceedings of the 10th ACM Workshop on Web Information and Data Management, WIDM '08, Co-located with the ACM 17th Conference on Information and Knowledge Management, CIKM '08. 2008. pp. 133-140 (International Conference on Information and Knowledge Management, Proceedings).
@inproceedings{95382ffa40e6482dbdbec9352f9b833e,
title = "Personalized ranking for digital libraries based on log analysis",
abstract = "Given the exponential increase of indexable context on the Web, ranking is an increasingly difficult problem in information retrieval systems. Recent research shows that implicit feedback regarding user preferences can be extracted from web access logs in order to increase ranking performance. We analyze the implicit user feedback from access logs in the CiteSeer academic search engine and show how site structure can better inform the analysis of clickthrough feedback providing accurate personalized ranking services tailored to individual information retrieval systems. Experiment and analysis shows that our proposed method is more accurate on predicting user preferences than any non-personalized ranking methods when user preferences are stable over time. We compare our method with several non-personalized ranking methods including ranking SVMlight as well as several ranking functions specific to the academic document domain. The results show that our ranking algorithm can reach 63.59{\%} accuracy in comparison to 50.02{\%} for ranking SVMlight and below 43{\%} for all other single feature ranking methods. We also show how the derived personalized ranking vectors can be employed for other ranking-related purposes such as recommendation systems.",
author = "Yang Sun and Huajing Li and Councill, {Isaac G.} and Jian Huang and Lee, {Wang Chien} and Giles, {C. Lee}",
year = "2008",
month = "12",
day = "1",
doi = "10.1145/1458502.1458524",
language = "English (US)",
isbn = "9781605582603",
series = "International Conference on Information and Knowledge Management, Proceedings",
pages = "133--140",
booktitle = "Proceedings of the 10th ACM Workshop on Web Information and Data Management, WIDM '08, Co-located with the ACM 17th Conference on Information and Knowledge Management, CIKM '08",

}

Sun, Y, Li, H, Councill, IG, Huang, J, Lee, WC & Giles, CL 2008, Personalized ranking for digital libraries based on log analysis. in Proceedings of the 10th ACM Workshop on Web Information and Data Management, WIDM '08, Co-located with the ACM 17th Conference on Information and Knowledge Management, CIKM '08. International Conference on Information and Knowledge Management, Proceedings, pp. 133-140, 10th ACM Workshop on Web Information and Data Management, WIDM '08, Co-located with the ACM 17th Conference on Information and Knowledge Management, CIKM '08, Napa Valley, CA, United States, 10/26/08. https://doi.org/10.1145/1458502.1458524

Personalized ranking for digital libraries based on log analysis. / Sun, Yang; Li, Huajing; Councill, Isaac G.; Huang, Jian; Lee, Wang Chien; Giles, C. Lee.

Proceedings of the 10th ACM Workshop on Web Information and Data Management, WIDM '08, Co-located with the ACM 17th Conference on Information and Knowledge Management, CIKM '08. 2008. p. 133-140 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Personalized ranking for digital libraries based on log analysis

AU - Sun, Yang

AU - Li, Huajing

AU - Councill, Isaac G.

AU - Huang, Jian

AU - Lee, Wang Chien

AU - Giles, C. Lee

PY - 2008/12/1

Y1 - 2008/12/1

N2 - Given the exponential increase of indexable context on the Web, ranking is an increasingly difficult problem in information retrieval systems. Recent research shows that implicit feedback regarding user preferences can be extracted from web access logs in order to increase ranking performance. We analyze the implicit user feedback from access logs in the CiteSeer academic search engine and show how site structure can better inform the analysis of clickthrough feedback providing accurate personalized ranking services tailored to individual information retrieval systems. Experiment and analysis shows that our proposed method is more accurate on predicting user preferences than any non-personalized ranking methods when user preferences are stable over time. We compare our method with several non-personalized ranking methods including ranking SVMlight as well as several ranking functions specific to the academic document domain. The results show that our ranking algorithm can reach 63.59% accuracy in comparison to 50.02% for ranking SVMlight and below 43% for all other single feature ranking methods. We also show how the derived personalized ranking vectors can be employed for other ranking-related purposes such as recommendation systems.

AB - Given the exponential increase of indexable context on the Web, ranking is an increasingly difficult problem in information retrieval systems. Recent research shows that implicit feedback regarding user preferences can be extracted from web access logs in order to increase ranking performance. We analyze the implicit user feedback from access logs in the CiteSeer academic search engine and show how site structure can better inform the analysis of clickthrough feedback providing accurate personalized ranking services tailored to individual information retrieval systems. Experiment and analysis shows that our proposed method is more accurate on predicting user preferences than any non-personalized ranking methods when user preferences are stable over time. We compare our method with several non-personalized ranking methods including ranking SVMlight as well as several ranking functions specific to the academic document domain. The results show that our ranking algorithm can reach 63.59% accuracy in comparison to 50.02% for ranking SVMlight and below 43% for all other single feature ranking methods. We also show how the derived personalized ranking vectors can be employed for other ranking-related purposes such as recommendation systems.

UR - http://www.scopus.com/inward/record.url?scp=77951107268&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77951107268&partnerID=8YFLogxK

U2 - 10.1145/1458502.1458524

DO - 10.1145/1458502.1458524

M3 - Conference contribution

SN - 9781605582603

T3 - International Conference on Information and Knowledge Management, Proceedings

SP - 133

EP - 140

BT - Proceedings of the 10th ACM Workshop on Web Information and Data Management, WIDM '08, Co-located with the ACM 17th Conference on Information and Knowledge Management, CIKM '08

ER -

Sun Y, Li H, Councill IG, Huang J, Lee WC, Giles CL. Personalized ranking for digital libraries based on log analysis. In Proceedings of the 10th ACM Workshop on Web Information and Data Management, WIDM '08, Co-located with the ACM 17th Conference on Information and Knowledge Management, CIKM '08. 2008. p. 133-140. (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/1458502.1458524