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Front. Inform. Technol. Electron. Eng.  2014, Vol. 15 Issue (11): 984-998    DOI: 10.1631/jzus.C1300374
    
基于主题回归和关联矩阵分解的科技文献推荐
Ming Yang, Ying-ming Li, Zhongfei (Mark) Zhang
Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
Scientific articles recommendation with topic regression and relational matrix factorization
Ming Yang, Ying-ming Li, Zhongfei (Mark) Zhang
Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
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摘要： 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    
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.
Key words: Matrix factorization    Probabilistic topic modeling    Relational matrix factorization    Recommender system
收稿日期: 2013-12-22 出版日期: 2014-11-07
CLC:  TP391  
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Ming Yang
Ying-ming Li
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Ming Yang, Ying-ming Li, Zhongfei (Mark) Zhang. Scientific articles recommendation with topic regression and relational matrix factorization. Front. Inform. Technol. Electron. Eng., 2014, 15(11): 984-998.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1300374    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2014/V15/I11/984

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