S. Oh. Matrix completion: Fundamentak limits and efficient al- gorithms. Stanford University, 2010.
has been cited by the following article:
- TITLE: 基于迁移学习的单类协同过滤算法One Class Collaborative Filtering Algorithm Based on Transfer Learning
- AUTHORS: 罗圣美, 林运祯, 叶小伟, 文海龙
- KEYWORDS: 推荐系统；协同过滤；单类；迁移学习<br>Recommendation; Collaborative Filtering; One Class; Transfer Learning
JOURNAL NAME: Hans Journal of Data Mining
Oct 15, 2014
- ABSTRACT: 协同过滤算法是现在个性化推荐领域流行的算法。对常见的推荐问题，协同过滤算法已有成熟的实现。单类协同过滤问题是推荐领域的一个新问题，其数据特征导致其不适用于常见的协同过滤算法。本文研究了基于加权矩阵分解的单类协同过滤算法，并对其进行基于迁移学习的改进。通过在真实数据集上的验证，证明其效果优于传统的单类协同过滤算法。
Collaborative filtering is a useful algorithm for problems of personalized recommendation. For these prob-lems, there are many mature collaborative filtering algorithms. One class collaborative filtering is a new field of per-sonalized recommendation. Because of its data characteristics, common collaborative filtering algorithms have a lot of defects in the field of one class collaborative filtering. We studied the algorithm based on weighted matrix decomposi-tion, and optimized this algorithm by transfer learning. We prove the improvement of this optimization by experiments.