基于个性化主题模型的用户生成内容推荐

张巍; 庄家煜; 雍熙; 李建扣; 陈威; 李哲敏

doi:10.1631/FITEE.1500402

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基于个性化主题模型的用户生成内容推荐

常规文章 | Updated：2022-05-19

- 基于个性化主题模型的用户生成内容推荐
- Personalized topic modeling for recommending user-generated content
- 信息与电子工程前沿（英文） 2017年18卷第5期页码：708-718
- Affiliations：
  
  1State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
  2School of Information Science and Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
  3Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  4Key Laboratory of Agri-information Service Technology, Ministry of Agriculture, Beijing 100081, China
  5Water Information Centre, Ministry of Water Resources, Beijing 100053, China
- Author bio：
  
  E-mail:lizhemin@caas.cn
- Funds：
  
  Project supported by the Monitoring Statistics Project on Agricultural and Rural Resources, MOA, China, the Innovative Talents Project, MOA, China, and the Science and Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences (No. CAASASTIP-2015-AI I-02)
- DOI：10.1631/FITEE.1500402
  中图分类号： TP393.0
- 收稿：1015-11-17，
  
  录用：2016-4-25，
  
  纸质出版：2017-05
- Accepted：
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张巍, 庄家煜, 雍熙, 等. 基于个性化主题模型的用户生成内容推荐[J]. 信息与电子工程前沿（英文）, 2017,18(5):708-718.

Wei ZHANG, Jia-yu ZHUANG, Xi YONG, et al. Personalized topic modeling for recommending user-generated content[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(5): 708-718.
张巍, 庄家煜, 雍熙, 等. 基于个性化主题模型的用户生成内容推荐[J]. 信息与电子工程前沿（英文）, 2017,18(5):708-718. DOI： 10.1631/FITEE.1500402.

Wei ZHANG, Jia-yu ZHUANG, Xi YONG, et al. Personalized topic modeling for recommending user-generated content[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(5): 708-718. DOI： 10.1631/FITEE.1500402.

摘要

互联网服务中有很多用户生成的内容（User-generated content

UGC），例如博客，微博等。在这些系统中，需要推荐算法来帮助用户过滤海量的内容。然而，传统的推荐模型没有考虑用户和内容之间的创作关系。本文中，我们验证了：通过引入创作关系信息，可以显著提高推荐算法的各项指标。基于层次主题模型和矩阵分解模型，我们构造了一个新的推荐模型。实验证明我们的新模型显著的优于其它已有模型，并且可以为用户和内容对应的主题给出直观解释。另外，由于从用户生成的内容我们可以推测其兴趣偏好，因此可以解决冷启动问题。

Abstract

User-generated content (UGC) such as blogs and twitters are exploding in modern Internet services. In such systems

recommender systems are needed to help people filter vast amount of UGC generated by other users. However

traditional recommendation models do not use user authorship of items. In this paper

we show that with this additional information

we can significantly improve the performance of recommendations. A generative model that combines hierarchical topic modeling and matrix factorization is proposed. Empirical results show that our model outperforms other state-of-the-art models

and can provide interpretable topic structures for users and items. Furthermore

since user interests can be inferred from their productions

recommendations can be made for users that do not have any ratings to solve the cold-start problem.

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