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CLC number: TP393.0

On-line Access: 2017-05-24

Received: 2015-11-17

Revision Accepted: 2016-04-25

Crosschecked: 2017-04-28

Cited: 0

Clicked: 3701

Citations:  Bibtex RefMan EndNote GB/T7714


Zhe-min Li


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.5 P.708-718


Personalized topic modeling for recommending user-generated content

Author(s):  Wei Zhang, Jia-yu Zhuang, Xi Yong, Jian-kou Li, Wei Chen, Zhe-min Li

Affiliation(s):  State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; more

Corresponding email(s):   lizhemin@caas.cn

Key Words:  User-generated content (UGC), Collaborative filtering (CF), Matrix factorization (MF), Hierarchical topic modeling

Wei Zhang, Jia-yu Zhuang, Xi Yong, Jian-kou Li, Wei Chen, Zhe-min Li. Personalized topic modeling for recommending user-generated content[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(5): 708-718.

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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.


概要: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。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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