Full Text:   <2915>

Summary:  <1898>

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: 6586

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhe-min Li

http://orcid.org/0000-0003-3170-0117

-   Go to

Article info.
Open peer comments

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

http://doi.org/10.1631/FITEE.1500402


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.

@article{title="Personalized topic modeling for recommending user-generated content",
author="Wei Zhang, Jia-yu Zhuang, Xi Yong, Jian-kou Li, Wei Chen, Zhe-min Li",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="5",
pages="708-718",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500402"
}

%0 Journal Article
%T Personalized topic modeling for recommending user-generated content
%A Wei Zhang
%A Jia-yu Zhuang
%A Xi Yong
%A Jian-kou Li
%A Wei Chen
%A Zhe-min Li
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 5
%P 708-718
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500402

TY - JOUR
T1 - Personalized topic modeling for recommending user-generated content
A1 - Wei Zhang
A1 - Jia-yu Zhuang
A1 - Xi Yong
A1 - Jian-kou Li
A1 - Wei Chen
A1 - Zhe-min Li
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 5
SP - 708
EP - 718
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500402


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.

基于个性化主题模型的用户生成内容推荐

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。

关键词:用户生成内容;协同过滤;矩阵分解;层次主题模型

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

Reference

[1]Agarwal, D., Chen, B.C., 2009. Regression-based latent factor models. Proc. 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.19-28.

[2]Agarwal, D., Chen, B.C., 2010. fLDA: matrix factorization through latent Dirichlet allocation. Proc. 3rd ACM Int. Conf. on Web Search and Data Mining, p.91-100.

[3]Blei, D.M., Ng, A.Y., Jordan, M.I., 2003. Latent Dirichlet allocation. J Mach. Learn. Res., 3:993-1022.

[4]Blei, D.M., Griffiths, T.L., Jordan, M.I., 2010. The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. J. ACM, 57(2):7.

[5]de Pessemier, T., Deryckere, T., Martens, L., 2011. Context aware recommendations for user-generated content on a social network site. Proc. 7th European Interactive Television Conf., p.133-136.

[6]Deshpande, M., Karypis, G., 2004. Item-based top-N recommendation algorithms. ACM Trans. Inform. Syst., 22(1): 143-177.

[7]Duchi, J., Shalev-Shwartz, S., Singer, Y., et al., 2008. Efficient projections onto the 1-ball for learning in high dimensions. Proc. 25th Int. Conf. on Machine Learning, p.272-279.

[8]Hu, Y., Koren, Y., Volinsky, C., 2008. Collaborative filtering for implicit feedback datasets. 8th IEEE Int. Conf. on Data Mining, p.263-272.

[9]Li, Y.M., Yang, M., Zhang, Z.F., 2013. Scientific articles recommendation. Proc. 22nd ACM Int. Conf. on Information and Knowledge Management, p.1147-1156.

[10]Linden, G., Smith, B., York, J., 2003. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Intern. Comput., 7(1):76-80.

[11]Lops, P., de Gemmis, M., Semeraro, G., 2011. Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., et al. (Eds.), Recommender Systems Handbook. Springer, Boston, p.73-105.

[12]Melville, P., Mooney, R.J., Nagarajan, R., 2002. Content-boosted collaborative filtering for improved recommendations. Proc. 8th National Conf. on Artificial Intelligence, p.187-192.

[13]Mooney, R.J., Roy, L., 2000. Content-based book recommending using learning for text categorization. Proc. 5th ACM Conf. on Digital Libraries, p.195-204.

[14]Pan, R., Zhou, Y., Cao, B., et al., 2008. One-class collaborative filtering. 8th IEEE Int. Conf. on Data Mining, p.502-511.

[15]Purushotham, S., Liu, Y., Kuo, C.C.J., 2012. Collaborative topic regression with social matrix factorization for recommendation systems. arXiv:1206.4684.

[16]Rendle, S., Freudenthaler, C., Gantner, Z., et al., 2009. BPR: Bayesian personalized ranking from implicit feedback. Proc. 25th Conference on Uncertainty in Artificial Intelligence, p.452-461.

[17]Salakhutdinov, R., Mnih, A., 2007. Probabilistic matrix factorization. Neural Information Processing Systems, p.1257-1264.

[18]Salakhutdinov, R., Mnih, A., 2008. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. Proc. 25th Int. Conf. on Machine Learning, p.880-887.

[19]Teh, Y.W., Jordan, M.I., Beal, M.J., et al., 2004. Sharing clusters among related groups: hierarchical Dirichlet processes. Neural Information Processing Systems, p.1385-1392.

[20]Veeramachaneni, S., Sona, D., Avesani, P., 2005. Hierarchical Dirichlet model for document classification. Proc. 22nd Int. Conf. on Machine Learning, p.928-935.

[21]Wang, C., Blei, D.M., 2011. Collaborative topic modeling for recommending scientific articles. Proc. 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.448-456.

[22]Xu, Y., Yin, J., 2015. Collaborative recommendation with user generated content. Eng. Appl. Artif. Intel., 45(C):281-294.

[23]Xu, Y., Chen, Z., Yin, J., et al., 2015. Learning to recommend with user generated content. Int. Conf. on Web-Age Information Management, p.221-232.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE