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Personalized topic modeling for recommending user-generated content
- 基于个性化主题模型的用户生成内容推荐
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 5, Pages: 708-718(2017)
- 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：
DOI：10.1631/FITEE.1500402
CLC： TP393.0Received：17 November 1015，
Accepted：25 April 2016，
Published：2017-05
- Accepted：
Scan QR Code
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.
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.
关键词
Keywords
references
D Agarwal , , , BC Chen . . Regression-based latent factor models . . 2009 . . Proc. 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining . . 19 - - 28 . . DOI: 10.1145/1557019.1557029 http://doi.org/10.1145/1557019.1557029 . .
D Agarwal , , , BC Chen . . fLDA: matrix factorization through latent Dirichlet allocation . . 2010 . . Proc. 3rd ACM Int. Conf. on Web Search and Data Mining . . 91 - - 100 . . DOI: 10.1145/1718487.1718499 http://doi.org/10.1145/1718487.1718499 . .
DM Blei , , , AY Ng , , , MI Jordan . . Latent Dirichlet allocation . . J Mach. Learn. Res. , , 2003 . . 3 993 - - 1022 . . . .
DM Blei , , , TL Griffiths , , , MI Jordan . . The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies . . J. ACM , , 2010 . . 57 ( ( 2 ): ): 7 DOI: 10.1145/1667053.1667056 http://doi.org/10.1145/1667053.1667056 . .
T de Pessemier , , , T Deryckere , , , L Martens . . Context aware recommendations for user-generated content on a social network site . . 2011 . . Proc. 7th European Interactive Television Conf. . . 133 - - 136 . . DOI: 10.1145/1542084.1542108 http://doi.org/10.1145/1542084.1542108 . .
M Deshpande , , , G Karypis . . Item-based top-N recommendation algorithms . . ACM Trans. Inform. Syst. , , 2004 . . 22 ( ( 1 ): ): 143 - - 177 . . DOI: 10.1145/963770.963776 http://doi.org/10.1145/963770.963776 . .
J Duchi , , , S Shalev-Shwartz , , , Y Singer , , , 等 . . Efficient projections onto the ℓ 1 -ball for learning in high dimensions . . 2008 . . Proc. 25th Int. Conf. on Machine Learning . . 272 - - 279 . . . .
Y Hu , , , Y Koren , , , C Volinsky . . Collaborative filtering for implicit feedback datasets . . 2008 . . 8th IEEE Int. Conf. on Data Mining . . 263 - - 272 . . DOI: 10.1109/ICDM.2008.22 http://doi.org/10.1109/ICDM.2008.22 . .
YM Li , , , M Yang , , , ZF Zhang . . Scientific articles recommendation . . 2013 . . Proc. 22nd ACM Int. Conf. on Information and Knowledge Management . . 1147 - - 1156 . . DOI: 10.1145/2505515.2505705 http://doi.org/10.1145/2505515.2505705 . .
G Linden , , , B Smith , , , J York . . Amazon.com recommendations: item-to-item collaborative filtering . . IEEE Intern. Comput. , , 2003 . . 7 ( ( 1 ): ): 76 - - 80 . . DOI: 10.1109/MIC.2003.1167344 http://doi.org/10.1109/MIC.2003.1167344 . .
P Lops , , , M de Gemmis , , , G Semeraro . . Content-based recommender systems: state of the art and trends. In: Ricci F, Rokach L, Shapira B, et al. (Eds.) . . 2011 . . Recommender Systems Handbook. Springer, Boston . . 73 - - 105 . . DOI: 10.1007/978-0-387-85820-3_3 http://doi.org/10.1007/978-0-387-85820-3_3 . .
P Melville , , , RJ Mooney , , , R Nagarajan . . Content- boosted collaborative filtering for improved recommendations . . 2002 . . Proc. 8th National Conf. on Artificial Intelligence . . 187 - - 192 . . . .
RJ Mooney , , , L Roy . . Content-based book recommending using learning for text categorization . . 2000 . . Proc. 5th ACM Conf. on Digital Libraries . . 195 - - 204 . . DOI: 10.1145/336597.336662 http://doi.org/10.1145/336597.336662 . .
R Pan , , , Y Zhou , , , B Cao , , , 等 . . One-class collaborative filtering . . 2008 . . 8th IEEE Int. Conf. on Data Mining . . 502 - - 511 . . DOI: 10.1109/ICDM.2008.16 http://doi.org/10.1109/ICDM.2008.16 . .
S Purushotham , , , Y Liu , , , CCJ Kuo . . Collaborative topic regression with social matrix factorization for recommendation systems . . arXiv , , 2012 . . 1206.4684 . .
S Rendle , , , C Freudenthaler , , , Z Gantner , , , 等 . . BPR: Bayesian personalized ranking from implicit feedback . . 2009 . . Proc. 25th Conference on Uncertainty in Artificial Intelligence . . 452 - - 461 . . . .
R Salakhutdinov , , , A Mnih . . Probabilistic matrix factorization . . 2007 . . Neural Information Processing Systems . . 1257 - - 1264 . . . .
R Salakhutdinov , , , A Mnih . . Bayesian probabilistic matrix factorization using Markov chain Monte Carlo . . 2008 . . Proc. 25th Int. Conf. on Machine Learning . . 880 - - 887 . . DOI: 10.1145/1390156.1390267 http://doi.org/10.1145/1390156.1390267 . .
YW Teh , , , MI Jordan , , , MJ Beal , , , 等 . . Sharing clusters among related groups: hierarchical Dirichlet processes . . 2004 . . Neural Information Processing Systems . . 1385 - - 1392 . . . .
S Veeramachaneni , , , D Sona , , , P Avesani . . Hierarchical Dirichlet model for document classification . . 2005 . . Proc. 22nd Int. Conf. on Machine Learning . . 928 - - 935 . . DOI: 10.1145/1102351.1102468 http://doi.org/10.1145/1102351.1102468 . .
C Wang , , , DM Blei . . Collaborative topic modeling for recommending scientific articles . . 2011 . . Proc. 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining . . 448 - - 456 . . DOI: 10.1145/2020408.2020480 http://doi.org/10.1145/2020408.2020480 . .
Y Xu , , , J Yin . . Collaborative recommendation with user generated content . . Eng. Appl. Artif. Intel. , , 2015 . . 45 ( ( C ): ): 281 - - 294 . . DOI: 10.1016/j.engappai.2015.07.012 http://doi.org/10.1016/j.engappai.2015.07.012 . .
Y Xu , , , Z Chen , , , J Yin , , , 等 . . Learning to recommend with user generated content . . 2015 . . Int. Conf. on Web-Age Information Management . . 221 - - 232 . . DOI: 10.1007/978-3-319-21042-1_18 http://doi.org/10.1007/978-3-319-21042-1_18 . .
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