Advertisement

Journal of Zhejiang University SCIENCE C

, Volume 15, Issue 11, pp 984–998 | Cite as

Scientific articles recommendation with topic regression and relational matrix factorization

  • Ming Yang
  • Ying-ming Li
  • Zhongfei (Mark) Zhang
Article
  • 140 Downloads

Abstract

In this paper we study the problem of recommending scientific articles to users in an online community with a new perspective of considering topic regression modeling and articles relational structure analysis simultaneously. First, we present a novel topic regression model, the topic regression matrix factorization (tr-MF), to solve the problem. The main idea of tr-MF lies in extending the matrix factorization with a probabilistic topic modeling. In particular, tr-MF introduces a regression model to regularize user factors through probabilistic topic modeling under the basic hypothesis that users share similar preferences if they rate similar sets of items. Consequently, tr-MF provides interpretable latent factors for users and items, and makes accurate predictions for community users. To incorporate the relational structure into the framework of tr-MF, we introduce relational matrix factorization. Through combining tr-MF with the relational matrix factorization, we propose the topic regression collective matrix factorization (tr-CMF) model. In addition, we also present the collaborative topic regression model with relational matrix factorization (CTR-RMF) model, which combines the existing collaborative topic regression (CTR) model and relational matrix factorization (RMF). From this point of view, CTR-RMF can be considered as an appropriate baseline for tr-CMF. Further, we demonstrate the efficacy of the proposed models on a large subset of the data from CiteULike, a bibliography sharing service dataset. The proposed models outperform the state-of-the-art matrix factorization models with a significant margin. Specifically, the proposed models are effective in making predictions for users with only few ratings or even no ratings, and support tasks that are specific to a certain field, neither of which has been addressed in the existing literature.

Key words

Matrix factorization Probabilistic topic modeling Relational matrix factorization Recommender system 

CLC number

TP391 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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. [doi:10.1145/1557019.1557029]CrossRefGoogle Scholar
  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. [doi:10.1145/1718487.1718499]CrossRefGoogle Scholar
  3. Bell, R.M., Koren, Y., 2007. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. Proc. 7th IEEE Int. Conf. on Data Mining, p.43–52. [doi:10.1109/ICDM.2007.90]Google Scholar
  4. Bell, R.M., Koren, Y., Volinsky, C., 2007. Modeling relationships at multiple scales to improve accuracy of large recommender systems. Proc. 13th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.95–104. [doi:10.1145/1281192.1281206]CrossRefGoogle Scholar
  5. Blei, D.M., Ng, A.Y., Jordan, M.I., 2003. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993–1022.MATHGoogle Scholar
  6. Dempster, A.P., Laird, N.M., Rubin, D.B., 1977. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B-Stat. Methdol., 39(1):1–38.MATHMathSciNetGoogle Scholar
  7. Ge, Y., Liu, Q., Xiong, H., et al., 2011. Cost-aware travel tour recommendation. Proc. 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.983–991. [doi:10.1145/2020408.2020568]Google Scholar
  8. Hu, Y., Koren, Y., Volinsky, C., 2008. Collaborative filtering for implicit feedback datasets. Proc. 8th IEEE Int. Conf. on Data Mining, p.263–272. [doi:10.1109/ICDM.2008.22]Google Scholar
  9. Jiang, M., Cui, P., Liu, R., et al., 2012. Social contextual recommendation. Proc. 21st ACM Int. Conf. on Information and Knowledge Management, p.45–54. [doi:10.1145/2396761.2396771]Google Scholar
  10. Jin, X., Zhou, Y., Mobasher, B., 2005. A maximum entropy web recommendation system: combining collaborative and content features. Proc. 11th ACM SIGKDD Int. Conf. on Knowledge Discovery in Data Mining, p.612–617. [doi:10.1145/1081870.1081945]Google Scholar
  11. Koren, Y., 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. Proc. 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.426–434. [doi:10.1145/1401890.1401944]Google Scholar
  12. Koren, Y., Bell, R.M., Volinsky, C., 2009. Matrix factorization techniques for recommender systems. Computer, 42(8):30–37. [doi:10.1109/MC.2009.263]CrossRefGoogle Scholar
  13. Lafferty, J.D., Blei, D.M., 2005. Correlated topic models. Advances in Neural Information Processing Systems, p.147–154.Google Scholar
  14. Mcauliffe, J.D., Blei, D.M., 2007. Supervised topic models. Advances in Neural Information Processing Systems, p.121–128.Google Scholar
  15. Melville, P., Mooney, R.J., Nagarajan, R., 2002. Contentboosted collaborative filtering for improved recommendations. AAAI, p.187–192.Google Scholar
  16. Pan, R., Zhou, Y., Cao, B., et al., 2008. One-class collaborative filtering. Proc. 8th IEEE Int. Conf. on Data Mining, p.502–511. [doi:10.1109/ICDM.2008.16]Google Scholar
  17. Paterek, A., 2007. Improving regularized singular value decomposition for collaborative filtering. Proc. KDD Cup Workshop, p.5–8.Google Scholar
  18. Purushotham, S., Liu, Y., Kuo, C.C.J., 2012. Collaborative topic regression with social matrix factorization for recommendation systems. Proc. 29th Int. Conf. on Machine Learning, p.759–766.Google Scholar
  19. Salakhutdinov, R., Mnih, A., 2007. Probabilistic matrix factorization. Advances in Neural Information Processing Systems, p.1257–1264.Google Scholar
  20. Salakhutdinov, R., Mnih, A., 2008. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. Proc. 25th Int. Conf. on Machine Learning, p.880–887. [doi:10.1145/1390156.1390267]Google Scholar
  21. Shan, H., Banerjee, A., 2010. Generalized probabilistic matrix factorizations for collaborative filtering. Proc. 10th IEEE Int. Conf. on Data Mining, p.1025–1030. [doi:10.1109/ICDM.2010.116]Google Scholar
  22. Si, L., Jin, R., 2004. Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model. Proc. 13th ACM Int. Conf. on Information and Knowledge Management, p.156–157. [doi:10.1145/1031171.1031201]Google Scholar
  23. Srebro, N., Jaakkola, T., 2003. Weighted low-rank approximations. Proc. 20th Int. Conf. on Machine Learning, p.720–727.Google Scholar
  24. Srebro, N., Rennie, J., Jaakkola, T., 2004. Maximum-margin matrix factorization. Advances in Neural Information Processing Systems, p.1329–1336.Google Scholar
  25. 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. [doi:10.1145/2020408.2020480]Google Scholar
  26. Weston, J., Wang, C., Weiss, R., et al., 2012. Latent collaborative retrieval. Proc. 29th Int. Conf. on Machine Learning, p.9–16.Google Scholar
  27. Yu, K., Schwaighofer, A., Tresp, V., 2003. Collaborative ensemble learning: combining collaborative and content-based information filtering via hierarchical bayes. Proc. 19th Conf. on Uncertainty in Artificial Intelligence, p.616–623.Google Scholar

Copyright information

© Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ming Yang
    • 1
  • Ying-ming Li
    • 1
  • Zhongfei (Mark) Zhang
    • 1
  1. 1.Department of Information Science and Electronic EngineeringZhejiang UniversityHangzhouChina

Personalised recommendations