基于迁移学习的单类协同过滤算法
One Class Collaborative Filtering Algorithm Based on Transfer Learning

作者: 罗圣美 , 叶小伟 :中兴通讯,南京; 林运祯 * , 文海龙 :清华大学软件学院,北京;

关键词: 推荐系统协同过滤单类迁移学习Recommendation Collaborative Filtering One Class Transfer Learning

摘要: 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。

Abstract: 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.

文章引用: 罗圣美 , 林运祯 , 叶小伟 , 文海龙 (2013) 基于迁移学习的单类协同过滤算法。 数据挖掘, 3, 12-17. doi: 10.12677/HJDM.2013.31003

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