基于用户特征迁移的协同过滤推荐

doi:10.3969/j.issn.1000-3428.2015.01.007

计算机工程 ›› 2015, Vol. 41 ›› Issue (1): 37-43. doi: 10.3969/j.issn.1000-3428.2015.01.007

基于用户特征迁移的协同过滤推荐

柯良文,王靖

华侨大学计算机科学与技术学院,福建厦门 361021

收稿日期:2014-02-19 修回日期:2014-03-15 出版日期:2015-01-15 发布日期:2015-01-16
作者简介:柯良文(1988-),男,硕士研究生,主研方向:数据挖掘,个性化推荐算法;王靖,副教授、博士。
基金资助:
国家自然科学基金资助项目(61370006);福建省高等学校杰出青年科研人才培育计划基金资助项目(11FJPY01);福建省高等学校新世纪优秀人才支持计划基金资助项目(2012-FJ-NCET-ZR01)

Collaborative Filtering Recommendation Based on User Feature Transfer

KE Liangwen,WANG Jing

School of Computer Science and Technology,Huaqiao University,Xiamen 361021,China

Received:2014-02-19 Revised:2014-03-15 Online:2015-01-15 Published:2015-01-16

摘要/Abstract

摘要： 为提高推荐系统在数据稀疏情况下的推荐质量,提出一种基于用户特征迁移的协同过滤推荐模型。利用矩阵分解技术提取辅助领域的用户特征,通过建立正则项约束的矩阵分解模型,将辅助领域的用户特征迁移到目标领域中,协助目标领域用户特征的学习,最终生成目标领域的用户推荐。设计快速收敛的Wiberg算法得到模型的最优解,并对实际应用中的可行性进行分析。通过对2个公开数据集的实验结果表明,该模型能够实现辅助领域用户特征的迁移,有效提高目标领域的推荐质量。

关键词: 数据稀疏, 用户特征迁移, 协同过滤, 矩阵分解, Wiberg算法

Abstract: In order to improve the recommendation quality of recommender system with data sparsity,this paper proposes a user collaborative filtering recommendation model based on feature transfer.Firstly,matrix factorization technology is applied to collect the user feature from the auxiliary domain.Secondly,it constructs a matrix factorization model with the constraint of regularization term,which is used to transfer the user feature learned from the auxiliary domain to the target domain,so as to help the learning of user feature in the target domain.Finally,user recommendation is made for the target domain.A fast convergence Wiberg algorithm is also designed for the model to get the optimal solution,whose feasibility is also discussed for practical application.Through the experiment on two real world data sets,the model can effectively transfer the user feature of source domain,and improve the quality of recommender system for target domain.

Key words: data sparsity, user feature transfer, collaborative filtering, matrix factorization, Wiberg algorithm

中图分类号:

TP311

柯良文,王靖. 基于用户特征迁移的协同过滤推荐[J]. 计算机工程, 2015, 41(1): 37-43.

KE Liangwen,WANG Jing. Collaborative Filtering Recommendation Based on User Feature Transfer[J]. Computer Engineering, 2015, 41(1): 37-43.

http://www.ecice06.com/CN/Y2015/V41/I1/37

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