Scientific articles recommendation with topic regression and relational matrix factorization

Ming YANG; Ying-ming LI; Zhongfei(Mark) ZHANG

doi:10.1631/jzus.C1300374

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Scientific articles recommendation with topic regression and relational matrix factorization

Computer & Automation | Updated：2022-05-19

- Scientific articles recommendation with topic regression and relational matrix factorization
- 基于主题回归和关联矩阵分解的科技文献推荐
- Frontiers of Information Technology & Electronic Engineering Vol. 15, Issue 11, Pages: 984-998(2014)
- 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：
- DOI：10.1631/jzus.C1300374
  CLC： TP391
- Received：22 December 2013，
  
  Revised：2014-10-15，
  
  Published：2014-11
- Accepted：
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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：

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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