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Exploit latent Dirichlet allocation for collaborative filtering |
Zhoujun LI1, Haijun ZHANG1,2( ), Senzhang WANG3,4, Feiran HUANG1, Zhenping LI2, Jianshe ZHOU5 |
1. State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China 2. School of Information, Beijing Wuzi University, Beijing 101149, China 3. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China 4. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 211106, China 5. Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China |
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Abstract Previous work on the one-class collaborative filtering (OCCF) problem can be roughly categorized into pointwise methods, pairwise methods, and content-based methods. A fundamental assumption of these approaches is that all missing values in the user-item rating matrix are considered negative. However, this assumption may not hold because the missing values may contain negative and positive examples. For example, a user who fails to give positive feedback about an item may not necessarily dislike it; he may simply be unfamiliar with it. Meanwhile, content-based methods, e.g. collaborative topic regression (CTR), usually require textual content information of the items, and thus their applicability is largely limited when the text information is not available. In this paper, we propose to apply the latent Dirichlet allocation (LDA) model on OCCF to address the above-mentioned problems. The basic idea of this approach is that items are regarded as words, users are considered as documents, and the user-item feedback matrix constitutes the corpus. Our model drops the strong assumption that missing values are all negative and only utilizes the observed data to predict a user’s interest. Additionally, the proposed model does not need content information of the items. Experimental results indicate that the proposed method outperforms previous methods on various ranking-oriented evaluation metrics. We further combine this method with a matrix factorizationbased method to tackle the multi-class collaborative filtering (MCCF) problem, which also achieves better performance on predicting user ratings.
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Keywords
latent Dirichlet allocation
one-class collaborative filtering
multi-class collaborative filtering
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Corresponding Author(s):
Haijun ZHANG
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Just Accepted Date: 05 September 2016
Online First Date: 12 December 2017
Issue Date: 02 May 2018
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|
1 |
Pan W K, Chen L. GBPR: group preference based Bayesian personalized ranking for one-class collaborative filtering. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013, 2691–2697
|
2 |
Pan R, Zhou Y H, Cao B, Liu N N, Lukose R, Scholz M, Yang Q. One-class collaborative filtering. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 502–511
https://doi.org/10.1109/ICDM.2008.16
|
3 |
Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 2009, 452–461
|
4 |
Li Y, Hu J, Zhai C X, Chen Y. Improving one-class collaborative filtering by incorporating rich user information. In: Proceedings of the 19th ACM Conference on Information and Knowledge Management. 2010, 959–968
https://doi.org/10.1145/1871437.1871559
|
5 |
Hu Y F, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 263–272
https://doi.org/10.1109/ICDM.2008.22
|
6 |
Zhang H J, Li Z J, Chen Y, Zhang X M, Wang S Z. Exploit latent Dirichlet allocation for one-class collaborative filtering. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management. 2014, 1991–1994
https://doi.org/10.1145/2661829.2661992
|
7 |
Li B, Yang Q, Xue X Y. Can movies and books collaborate? Crossdomain collaborative filtering for sparsity reduction. In: Proceedings of the International Joint Conference on Artificial Intelligence. 2009, 2052–2057
|
8 |
Hofmann T. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 2004, 22(1): 89–115
https://doi.org/10.1145/963770.963774
|
9 |
Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning. 2007, 791–798
https://doi.org/10.1145/1273496.1273596
|
10 |
Zhang H J, Liu C Y, Li Z J, Zhang X M. Collaborative filtering based on rating psychology. In: Proceedings of International Conference on Web-Age Information Management. 2013, 655–665
https://doi.org/10.1007/978-3-642-38562-9_67
|
11 |
Gu B, Sheng V S, Tay K Y, Romano W, Li S. Incremental support vector learning for ordinal regression. IEEE Transactions on Neural Networks and Learning Systems, 2014, 26(7): 1403–1416
https://doi.org/10.1109/TNNLS.2014.2342533
|
12 |
Gu B, Sun X M, Sheng V S. Structural minimax probability machine. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28(7): 1646–1656
https://doi.org/10.1109/TNNLS.2016.2544779
|
13 |
Ma T H, Zhou J J, Tang M L, Tian Y, Al-Dhelaan A, Al-Rodhaan M, Lee S. Social network and tag sources based augmenting collaborative recommender system. IEICE Transactions on Information and Systems, 2015, E98-D(4): 902–910
https://doi.org/10.1587/transinf.2014EDP7283
|
14 |
Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer, 2009, 42(8): 30–37
https://doi.org/10.1109/MC.2009.263
|
15 |
Ma H, Yang H X, Lyu M R, King I. SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management. 2008, 931–940
https://doi.org/10.1145/1458082.1458205
|
16 |
Funk S. Netflix update: try this at home. Blog Post Sifter. 2006
|
17 |
Srebro N, Jaakkola T. Weighted low-rank approximations. In: Proceedings of the 20th International Conference on Machine Learning. 2003, 720–727
|
18 |
Cremonesi P, Koren Y, Turrin R. Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the 4th ACM Conference on Recommender Systems. 2010, 39–46
https://doi.org/10.1145/1864708.1864721
|
19 |
Mnih A, Salakhutdinov R R. Probabilistic matrix factorization. In: Proceedings of International Conference on Machine Learning. 2012, 880–887
|
20 |
He H B, Garcia E A. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9): 1263–1284
https://doi.org/10.1109/TKDE.2008.239
|
21 |
Wang C, Blei D M. Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011, 448–456
https://doi.org/10.1145/2020408.2020480
|
22 |
Purushotham S, Liu Y, Kuo C C J. Collaborative topic regression with social matrix factorization for recommendation systems. Computer Science, 2012
|
23 |
Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation. Journal of Machine Learning Research, 2003, 3: 993–1022
|
24 |
Chen Y, Yin X S, Li Z J, Hu X H, Huang J X. A LDA-based approach to promoting ranking diversity for genomics information retrieval. BMC Genomics, 2012, 13(Suppl 3): 104–111
|
25 |
Wilson J, Chaudhury S, Lall B. Improving collaborative filtering based recommenders using topic modelling. In: Proceedings of IEEE/WIC/ACM International Joint Conference on Artificial Intelligence. 2014, 340–346
https://doi.org/10.1109/WI-IAT.2014.54
|
26 |
Hofmann T. Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 1999, 56–73
https://doi.org/10.1145/312624.312649
|
27 |
Heinrich G. Parameter estimation for text analysis. Technical Report. 2005
|
28 |
Gantner Z, Rendle S, Freudenthaler C, Schmidt-Thieme L. MyMediaLite: a free recommender system library. In: Proceedings of the 5th ACM Conference on Recommender Systems. 2011, 305–308
https://doi.org/10.1145/2043932.2043989
|
29 |
Newman D, Asuncion A U, Smyth P, Asuncion A U. Distributed inference for latent Dirichlet allocation. In: Proceedings of Conference on Neural Information Processing Systems. 2007, 1–6
|
30 |
Wang Y, Bai H, Stanton M, Chen W Y, Chang E Y. PLDA: parallel latent Dirichlet allocation for large-scale applications. In: Proceedings of International Conference on Algorithmic Aspects in Information and Management. 2009, 301–314
https://doi.org/10.1007/978-3-642-02158-9_26
|
31 |
Magnusson M, Jonsson L, Villani M, Broman D. Parallelizing LDA using partially collapsed Gibbs sampling. Statistics, 2015
|
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