Improving maximum margin matrix factorization

Improving maximum margin matrix factorization
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DOI：
10.1007/s10994-008-5073-7
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发表时间：
2008-09
期刊：
Machine Learning
影响因子：
7.5
通讯作者：
Markus Weimer;Alexandros Karatzoglou;Alex Smola
Markus Weimer;Alexandros Karatzoglou;Alex Smola
中科院分区：
计算机科学3区
文献类型：
--
作者：
Markus Weimer;Alexandros Karatzoglou;Alex Smola

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协同过滤是一种流行的个性化产品推荐方法。最大保证金矩阵分解（MMMF）已被提出作为一个成功的学习方法，这项任务，最近已扩展到结构化的排名损失。在本文中，我们讨论了一些扩展MMMF通过引入偏移项，项目相关的正则化和推荐图上的图核。我们证明了图核与Mnih和Salakhutdinov最近的MMMF扩展（Advances in Neural Information Processing Systems 20，2008）之间的等价性。实验评估所引入的扩展表明，改进的性能比原来的MMMF制定。
Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful learning approach to this task and has been recently extended to structured ranking losses. In this paper we discuss a number of extensions to MMMF by introducing offset terms, item dependent regularization and a graph kernel on the recommender graph. We show equivalence between graph kernels and the recent MMMF extensions by Mnih and Salakhutdinov (Advances in Neural Information Processing Systems 20, 2008). Experimental evaluation of the introduced extensions show improved performance over the original MMMF formulation.