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
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"
}
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%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
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SP - 708
EP - 718
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
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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.
[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 ℓ1-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.
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