skip to main content
10.1145/1454008.1454064acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
research-article

Leveraging aggregate ratings for improving predictive performance of recommender systems

Published: 23 October 2008 Publication History

Abstract

One of the key problems in recommender systems is accurate estimation of unknown ratings of individual items for individual users in terms of the previously specified ratings and other characteristics of items and users. In this thesis, we investigate a way of improving estimations of individual ratings using externally provided properties of aggregate ratings for groups of items and users, such as an externally specified average rating of action movies provided by graduate students or externally specified standard deviation of ratings for comedy movies.

References

[1]
G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowledge and Data Engineering, 17(6), 2005.
[2]
A. Ansari, S. Essegaier, and R. Kohli. Internet Recommendation Systems. Journal of Marketing Research, 37(3):363--375, 2000.
[3]
J. Bennett and S. Lanning. The Netflix Prize. Proceedings of KDD Cup and Workshop 2007, 2007.
[4]
IMDB. http://www.imdb.com.
[5]
S. W. Raudenbush and A. S. Bryk. Hierarchical Linear Models: Applications and Data Analysis Methods. Sage Publications, Inc, 2001.
[6]
B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th international conference on World Wide Web, pages 285--295, 2001.
[7]
A. Schwaighofer, V. Tresp, and K. Yu. Learning Gaussian Process Kernels via Hierarchical Bayes. Advances in Neural Information Processing Systems, 17:1209--1216.
[8]
A. Umyarov and A. Tuzhilin. Leveraging aggregate ratings for better recommendations. Procs of the 2007 ACM conf. on RecSys, pages 161--164, 2007.
[9]
A. Umyarov and A. Tuzhilin. Leveraging aggregate ratings for improving predictive performance of recommender systems. Working paper. Stern School of Business. New York University. CeDER-08-03, 2008.

Cited By

View all
  • (2009)Improving rating estimation in recommender systems using aggregation- and variance-based hierarchical modelsProceedings of the third ACM conference on Recommender systems10.1145/1639714.1639722(37-44)Online publication date: 23-Oct-2009

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
October 2008
348 pages
ISBN:9781605580937
DOI:10.1145/1454008
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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2008

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. aggregate ratings
  2. collaborative filtering
  3. hierarchical models
  4. predictive models
  5. recommender systems

Qualifiers

  • Research-article

Conference

RecSys08: ACM Conference on Recommender Systems
October 23 - 25, 2008
Lausanne, Switzerland

Acceptance Rates

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 22 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2009)Improving rating estimation in recommender systems using aggregation- and variance-based hierarchical modelsProceedings of the third ACM conference on Recommender systems10.1145/1639714.1639722(37-44)Online publication date: 23-Oct-2009

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media