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CLC number: TP391

On-line Access: 2014-11-07

Received: 2013-12-22

Revision Accepted: 2014-05-05

Crosschecked: 2014-10-15

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Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.11 P.984-998

http://doi.org/10.1631/jzus.C1300374


Scientific articles recommendation with topic regression and relational matrix factorization


Author(s):  Ming Yang, Ying-ming Li, Zhongfei (Mark) Zhang

Affiliation(s):  Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   cauchym@zju.edu.cn, liymn@zju.edu.cn, zhongfei@zju.edu.cn

Key Words:  Matrix factorization, Probabilistic topic modeling, Relational matrix factorization, Recommender system


Ming Yang, Ying-ming Li, Zhongfei (Mark) Zhang. Scientific articles recommendation with topic regression and relational matrix factorization[J]. Journal of Zhejiang University Science C, 2014, 15(11): 984-998.

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Abstract: 
In this paper we study the problem of recommending scientific articles to users in an online community with a new perspective of considering topic regression modeling and articles relational structure analysis simultaneously. First, we present a novel topic regression model, the topic regression matrix factorization (tr-MF), to solve the problem. The main idea of tr-MF lies in extending the matrix factorization with a probabilistic topic modeling. In particular, tr-MF introduces a regression model to regularize user factors through probabilistic topic modeling under the basic hypothesis that users share similar preferences if they rate similar sets of items. Consequently, tr-MF provides interpretable latent factors for users and items, and makes accurate predictions for community users. To incorporate the relational structure into the framework of tr-MF, we introduce relational matrix factorization. Through combining tr-MF with the relational matrix factorization, we propose the topic regression collective matrix factorization (tr-CMF) model. In addition, we also present the collaborative topic regression model with relational matrix factorization (CTR-RMF) model, which combines the existing collaborative topic regression (CTR) model and relational matrix factorization (RMF). From this point of view, CTR-RMF can be considered as an appropriate baseline for tr-CMF. Further, we demonstrate the efficacy of the proposed models on a large subset of the data from CiteULike, a bibliography sharing service dataset. The proposed models outperform the state-of-the-art matrix factorization models with a significant margin. Specifically, the proposed models are effective in making predictions for users with only few ratings or even no ratings, and support tasks that are specific to a certain field, neither of which has been addressed in the existing literature.

It's a good paper!

基于主题回归和关联矩阵分解的科技文献推荐

研究目的:利用社交网络上科技文献的关联关系数据,进行基于矩阵分解主题模型的训练,从而更准确地推荐科技文献。
创新要点:在现有基于矩阵分解主题模型的基础上,引入科技文献数据之间的关联关系信息,从而更精确地学习数据的关联关系,提高了科技文献推荐准确率。
研究方法:着眼于主题回归模型与矩阵分解方法的结合使用,利用这两种方法在推荐系统中的应用,提出了一系列基于矩阵分解的主题模型。在CiteULike数据集上对所提出的模型进行验证。一方面,提出主题回归矩阵分解模型tr-MF(图1)。该模型对用户进行主题建模,并同时对评分利用矩阵分解构建用户与项目之间的关系。另一方面,为了有效利用科技文献之间的相关关系,提出协同主题回归相关矩阵分解模型CTR-RMF(图2)。在对文献使用主题回归和矩阵分解方法的基础上,该模型引入文献之间的关联关系进行学习。在上述两个模型基础上,提出主题回归合同矩阵分解模型tr-CMF(图3)。该模型以tr-MF为基础,进而为文献引入关联关系进行学习。最后,在CiteULike数据集上对本文提出的模型在不同特征维度(图4)、不同模型正则参数(图5,6)、不同用户活跃度(图7)等条件下同现有模型推荐准确率进行了全面比较。
重要结论:引入科技文献之间的关联关系,结合主题回归和矩阵分解方法,能够有效提升科技文献推荐准确率。

关键词:矩阵分解;概率主题建模;相关矩阵分解;推荐系统

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