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山东大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 96-102.doi: 10.6040/j.issn.1672-3961.0.2017.404

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一种融合社交网络的叠加联合聚类推荐模型

读习习,刘华锋,景丽萍*   

  1. 北京交通大学交通数据分析与挖掘北京市重点实验室, 北京 100044
  • 收稿日期:2017-08-23 出版日期:2018-06-20 发布日期:2017-08-23
  • 通讯作者: 景丽萍(1978— ),女,河南南阳人,博士,教授,主要研究方向为机器学习与数据挖掘. E-mail:lpjing@bjtu.edu.cn E-mail:15120391@bjtu.edu.cn
  • 作者简介:读习习(1990— ),女,山东济宁人,硕士研究生,主要研究方向为智能推荐. E-mail:15120391@bjtu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61370129,61375062,61632004);长江学者和创新团队发展计划资助项目(IRT201206)

An additive co-clustering for recommendation of integrating social network

DU Xixi, LIU Huafeng, JING Liping*   

  1. Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
  • Received:2017-08-23 Online:2018-06-20 Published:2017-08-23

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摘要： 为解决用户冷启动问题并提高推荐算法的评分预测精度,提出一种融合社交网络的叠加联合聚类推荐模型(SN-ACCRec),将用户社交关系融合到对评分矩阵的用户聚类中。根据社交关系理论分析用户社交关系,采用模糊C均值聚类的思想划分用户块,并利用k均值算法对评分矩阵的产品聚类,得到一次联合聚类结果。通过迭代方式获取用户和产品多层联合聚类结果,不断叠加多层聚类结果来近似评分矩阵,预期先后得到用户和产品的泛化和细化类别,实现对评分矩阵中缺失值的预测。采用十重交叉验证法对模型评估,试验结果表明,该模型有效降低了推荐中的平均绝对误差(mean absolute error, MAE)和均方根误差(root mean square error, RMSE),同时在冷启动用户上也表现出了较好地推荐性能。

关键词: 社交网络, 个性化推荐, 联合聚类

Abstract: In order to solve the problem of user cold start problem and improve the prediction accuracy of recommendation algorithm, an additive co-clustering recommendation model combining social networks(SN-ACCRec)was proposed, which integrated user social relations into user clustering of rating matrix. According to the social relations theory analysis of users, user blocks was divided with the idea of fuzzy C means clustering, and a co-clustering result was acquired by clusters items on rating matrix according to k-means algorithm. The general and specific categories was gotten by generating the user and item additive co-clustering results in an iterative method and pedict the missing values. The model was evaluated using ten fold cross validation method, and experimental results showed that this model could reduce the average absolute error(MAE)and the root mean square error(RMSE), which also showed a better recommendation performance in the cold start users.

Key words: personalized recommendation, social networks, co-clustering

中图分类号: 

  • TP311
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