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

doi:10.1631/jzus.C1300374

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基于主题回归和关联矩阵分解的科技文献推荐

计算机与自动化 | Updated：2022-05-19

- 基于主题回归和关联矩阵分解的科技文献推荐
- Scientific articles recommendation with topic regression and relational matrix factorization
- 信息与电子工程前沿（英文） 2014年15卷第11期页码：984-998
- Affiliations：
  
  Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  [ "Ming YANG, E-mail: cauchym@zju.edu.cn" ]
  [ "Ying-ming LI, E-mail: liymn@zju.edu.cn" ]
  [ "Zhongfei (Mark) ZHANG, E-mail: zhongfei@zju.edu.cn" ]
- Funds：
  
  Project supported by the National Basic Research Program (973) of China (No. 2012CB316400), Zhejiang University–Alibaba Financial Joint Lab, Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis, and the National Science Foundation of the United States (Nos. IIS-0812114 and CCF-1017828)
- DOI：10.1631/jzus.C1300374
  中图分类号： TP391
- 收稿：2013-12-22，
  
  修回：2014-10-15，
  
  纸质出版：2014-11
- Accepted：
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基于主题回归和关联矩阵分解的科技文献推荐[J]. 信息与电子工程前沿（英文）, 2014,15(11):984-998.

Ming YANG, Ying-ming LI, Zhongfei(Mark) ZHANG. Scientific articles recommendation with topic regression and relational matrix factorization[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(11): 984-998.
基于主题回归和关联矩阵分解的科技文献推荐[J]. 信息与电子工程前沿（英文）, 2014,15(11):984-998. DOI： 10.1631/jzus.C1300374.

Ming YANG, Ying-ming LI, Zhongfei(Mark) ZHANG. Scientific articles recommendation with topic regression and relational matrix factorization[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(11): 984-998. DOI： 10.1631/jzus.C1300374.

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。

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.

关键词

Keywords

references

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