NDLI: Maximum margin matrix factorization for code recommendation

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Maximum margin matrix factorization for code recommendation

Content Provider	ACM Digital Library
Author	Karatzoglou, Alexandros Bruch, Marcel Weimer, Markus
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.
Starting Page	309
Ending Page	312
Page Count	4
File Format	PDF
ISBN	9781605584355
DOI	10.1145/1639714.1639775
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-10-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Collaborative filtering Matrix factorization
Content Type	Text
Resource Type	Article

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