skip to main content
10.1145/1639714.1639775acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
short-paper

Maximum margin matrix factorization for code recommendation

Published: 23 October 2009 Publication History

Abstract

Code recommender systems ease the use and learning of software frameworks and libraries by recommending calls based on already present code. Typically, code recommender tools have been based on rather simple rule based systems while many of the recent advances in Recommender Systems and Collaborative Filtering have been largely focused on rating data. While many of these advances can be incorporated in the code recommendation setting this problem also brings considerable challenges of its own. In this paper, we extend state-of-the-art collaborative filtering technology, namely Maximum Margin Matrix Factorization (MMMF) to this interesting application domain and show how to deal with the challenges posed by this problem. To this end, we introduce two new loss functions to the MMMF model. While we focus on code recommendation in this paper, our contributions and the methodology we propose can be of use in almost any collaborative setting that can be represented as a binary interaction matrix. We evaluate the algorithm on real data drawn from the Eclipse Open Source Project. The results show a significant improvement over current rule-based approaches.

References

[1]
M. Bruch, T. Schafer, and M. Mezini. FrUiT: IDE support for framework understanding. In Proceedings of the OOPSLA Workshop on Eclipse Technology Exchange, pages 55{59. ACM Press, 2006.
[2]
W. S. Lee and B. Liu. Learning with positive and unlabeled examples using weighted logistic regression. In Proceedings of the 20th International Conference on Machine Learning (ICML 2003). AAAI Press, 2003.
[3]
N. Srebro and T. Jaakkola. Weighted low-rank approximations. In Proceedings of the 20th International Conference on Machine Learning (ICML 2003), pages 720 -- 727. AAAI Press, 2003.
[4]
N. Srebro, J. Rennie, and T. Jaakkola. Maximum-margin matrix factorization. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, Cambridge, MA, 2005. MIT Press.
[5]
M. Weimer, A. Karatzoglou, and A. Smola. Improving maximum margin matrix factorization. Machine Learning, 72(3), September 2008.

Cited By

View all

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
RecSys '09: Proceedings of the third ACM conference on Recommender systems
October 2009
442 pages
ISBN:9781605584355
DOI:10.1145/1639714
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

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2009

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. collaborative filtering
  2. matrix factorization

Qualifiers

  • Short-paper

Conference

RecSys '09
Sponsor:
RecSys '09: Third ACM Conference on Recommender Systems
October 23 - 25, 2009
New York, New York, USA

Acceptance Rates

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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)5
  • Downloads (Last 6 weeks)0
Reflects downloads up to 04 Jan 2025

Other Metrics

Citations

Cited By

View all

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media