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Exploit latent Dirichlet allocation for collaborative filtering

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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 factorization-based method to tackle the multi-class collaborative filtering (MCCF) problem, which also achieves better performance on predicting user ratings.

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Acknowledgments

We greatly appreciate Weike Pan for his codes of algorithm GBPR[1], which makes us able to evaluate the algorithm more efficiently and more fairly. This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. 61370126, 61672081, 71540028, 61571052, 61602237), National High-tech R&D Program of China (2015AA016004), Beijing Advanced Innovation Center for Imaging Technology (BAICIT-2016001), the Fund of the State Key Laboratory of Software Development Environment (SKLSDE-2013ZX-19), the Fund of Beijing Social Science (14JGC103), the Statistics Research Project of National Bureau (2013LY055), and the Fund of Beijing Wuzi University, China (GJB20141002).

Author information

Authors and Affiliations

  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China

    Zhoujun Li, Haijun Zhang & Feiran Huang

  2. School of Information, Beijing Wuzi University, Beijing, 101149, China

    Haijun Zhang & Zhenping Li

  3. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Senzhang Wang

  4. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 211106, China

    Senzhang Wang

  5. Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, 100048, China

    Jianshe Zhou

Authors
  1. Zhoujun Li
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  2. Haijun Zhang
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  3. Senzhang Wang
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  4. Feiran Huang
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  5. Zhenping Li
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Corresponding author

Correspondence to Haijun Zhang.

Additional information

Zhoujun Li received the BS degree in the School of Computer Science, Wuhan University, China in 1984, and the MS and PhD degrees in the School of Computer Science, National University of Defense Technology, China. Currently, he is working as a professor of Beihang University, China. His research interests include data mining, information retrieval, and information security. He is a member of the IEEE.

Haijun Zhang received the BS and MS degrees in management science from the China University of Mining and Technology, China in 1997 and 2004 respectively. He received his PhD degree from the School of Computer Science and Engineering, Beihang Univeristy, China in 2016. He is working at School of Information, Beijing Wuzi University, China since 2004. His major interests are data mining and recommendation system.

Senzhang Wang received his PhD degree from Beihang Univeristy, China in 2015. He is currently an assistant professor at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China. His research focus is on data mining, social computing, big data, and urban computing. He has published more than 30 paper in premier conferences and journals in computer science including SIGKDD, AAAI, SDM, ACM SIGSPAIAL, ECML-PKDD, ACM Transactions on Intelligent Systems and Technology, Knowledge and Information Systems, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Multimedia, et al.

Feiran Huang received his bachelor degree in Central South University, China in 2011. He is currently the PhD candidate of the School of Computer Science and Engineering, Beihang University, China. His research interests include data mining and social media analysis.

Zhenping Li received her bachelor and master degrees in operations research from Shandong University, China in 1989 and 1994 respectively. In 2004, she received her doctoral degree in operations research from Chinese Academy of Sciences, China. Her research interests include the complex network and intelligent algorithm. She is now a professor in School of Information at Beijing Wuzi University, China.

Jianshe Zhou received the PhD degree from Wuhan University, China in 2002. He is now a professor in Capital Normal University, China. His research interests include linguistics, logic, and language intelligence.

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Li, Z., Zhang, H., Wang, S. et al. Exploit latent Dirichlet allocation for collaborative filtering. Front. Comput. Sci. 12, 571–581 (2018). https://doi.org/10.1007/s11704-016-6078-1

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