A Minimax Game for Generative and Discriminative Sample Models for Recommendation

  • Zongwei Wang
  • Min GaoEmail author
  • Xinyi Wang
  • Junliang Yu
  • Junhao Wen
  • Qingyu Xiong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11440)


Recommendation systems often fail to live up to expectations in real situations because of the lack of user feedback, known as the data sparsity problem. A large number of existing recommendation methods resort to side information to gain a performance improvement. However, these methods are either too complicated to follow or time-consuming. To alleviate the data sparsity problem, in this paper we propose UGAN, which is a general adversarial framework for recommendation tasks and consists of a generative model and a discriminative model. In UGAN, the generative model, acts as an attacker to cheat the discriminative model to capture the pattern of the original data input and generate similar user profiles, while the counterpart, the discriminative model aims to distinguish the forged samples from the real data. By competing with each other, two model are alternatively updated like playing a minimax game until the generative model has learned the original data distribution. The experimental results on two real-world datasets, Movielens and Douban, show that the user profiles forged by UGAN can be easily integrated into a wide range of recommendation methods and significantly improve their performance, which provides a promising way to mitigate the adverse impact of missing data.


Recommendation system Data sparsity Generative Adversarial Network Minimax game Adversarial training 


  1. 1.
    Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. CoRR abs/1701.07875 (2017)Google Scholar
  2. 2.
    Bodnar, C.: Text to image synthesis using generative adversarial networks. CoRR abs/1805.00676 (2018)Google Scholar
  3. 3.
    Breese, J.S., Heckerman, D., Kadie, C.M.: Empirical analysis of predictive algorithms for collaborative filtering. CoRR abs/1301.7363 (2013)Google Scholar
  4. 4.
    Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, 7–13 December 2015, pp. 2650–2658 (2015)Google Scholar
  5. 5.
    Goodfellow, I.J., et al.: Generative adversarial networks. CoRR abs/1406.2661 (2014)Google Scholar
  6. 6.
    He, R., McAuley, J.: Fusing similarity models with Markov chains for sparse sequential recommendation. In: IEEE 16th International Conference on Data Mining, ICDM 2016, Barcelona, Spain, 12–15 December 2016, pp. 191–200 (2016)Google Scholar
  7. 7.
    Hsieh, C., Yang, L., Wei, H., Naaman, M., Estrin, D.: Immersive recommendation: news and event recommendations using personal digital traces. In: Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, 11–15 April 2016, pp. 51–62 (2016)Google Scholar
  8. 8.
    Huang, Z., Chen, H., Zeng, D.D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst. 22(1), 116–142 (2004)CrossRefGoogle Scholar
  9. 9.
    Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-n recommender systems. In: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, 11–14 August 2013, pp. 659–667 (2013)Google Scholar
  10. 10.
    Khoshneshin, M., Street, W.N.: Collaborative filtering via Euclidean embedding. In: Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, Barcelona, Spain, 26–30 September 2010, pp. 87–94 (2010)Google Scholar
  11. 11.
    Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)CrossRefGoogle Scholar
  12. 12.
    Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. CoRR abs/cs/0702144 (2007)Google Scholar
  13. 13.
    Li, W., et al.: Social recommendation using Euclidean embedding. In: 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, AK, USA, 14–19 May 2017, pp. 589–595 (2017)Google Scholar
  14. 14.
    Lin, Z., Shi, Y., Xue, Z.: IDSGAN: generative adversarial networks for attack generation against intrusion detection. CoRR abs/1809.02077 (2018)Google Scholar
  15. 15.
    Liu, Y., Qin, Z., Wan, T., Luo, Z.: Auto-painter: cartoon image generation from sketch by using conditional wasserstein generative adversarial networks. Neurocomputing 311, 78–87 (2018)CrossRefGoogle Scholar
  16. 16.
    Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, Napa Valley, California, USA, 26–30 October, pp. 931–940 (2008)Google Scholar
  17. 17.
    Mirza, M., Osindero, S.: Conditional generative adversarial nets. CoRR abs/1411.1784 (2014)Google Scholar
  18. 18.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. CoRR abs/1205.2618 (2012)Google Scholar
  19. 19.
    Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, 5–9 June 2008, pp. 880–887 (2008)Google Scholar
  20. 20.
    Wang, J., et al.: IRGAN: a minimax game for unifying generative and discriminative information retrieval models. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, 7–11 August 2017, pp. 515–524 (2017)Google Scholar
  21. 21.
    Xiong, D., Liu, Q., Lin, S.: Maximum entropy based phrase reordering model for statistical machine translation. In: ACL 2006, 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, Sydney, Australia, 17–21 July 2006 (2006)Google Scholar
  22. 22.
    Yu, J., Gao, M., Li, J., Yin, H., Liu, H.: Adaptive implicit friends identification over heterogeneous network for social recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, 22–26 October 2018, pp. 357–366 (2018)Google Scholar
  23. 23.
    Yu, J., Gao, M., Rong, W., Song, Y., Xiong, Q.: A social recommender based on factorization and distance metric learning. IEEE Access 5, 21557–21566 (2017)CrossRefGoogle Scholar
  24. 24.
    Zhang, C., Yu, L., Wang, Y., Shah, C., Zhang, X.: Collaborative user network embedding for social recommender systems. In: Proceedings of the 2017 SIAM International Conference on Data Mining, Houston, Texas, USA, 27–29 April 2017, pp. 381–389 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zongwei Wang
    • 1
  • Min Gao
    • 1
    Email author
  • Xinyi Wang
    • 1
  • Junliang Yu
    • 1
  • Junhao Wen
    • 1
  • Qingyu Xiong
    • 1
  1. 1.School of Big Data and Software EngineeringChongqing UniversityChongqingChina

Personalised recommendations