%0 Journal Article
%T 基于迁移学习的单类协同过滤算法<br>One Class Collaborative Filtering Algorithm Based on Transfer Learning
%A 罗圣美
%A 林运祯
%A 叶小伟
%A 文海龙
%J Hans Journal of Data Mining
%P 12-17
%@ 2163-1468
%D 2013
%I Hans Publishing
%R 10.12677/HJDM.2013.31003
%X 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。<br />
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.
%K 推荐系统；协同过滤；单类；迁移学习<br>Recommendation
%K Collaborative Filtering
%K One Class
%K Transfer Learning
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=9374