An Adversarial Approach to Improve Long-Tail Performance in Neural Collaborative Filtering

ABSTRACT

In recent times, deep neural networks have found success in Collaborative Filtering (CF) based recommendation tasks. By parametrizing latent factor interactions of users and items with neural architectures, they achieve significant gains in scalability and performance over matrix factorization. However, the long-tail phenomenon in recommender performance persists on the massive inventories of online media or retail platforms. Given the diversity of neural architectures and applications, there is a need to develop a generalizable and principled strategy to enhance long-tail item coverage.

In this paper, we propose a novel adversarial training strategy to enhance long-tail recommendations for users with Neural CF (NCF) models. The adversary network learns the implicit association structure of entities in the feedback data while the NCF model is simultaneously trained to reproduce these associations and avoid the adversarial penalty, resulting in enhanced long-tail performance. Experimental results show that even without auxiliary data, adversarial training can boost long-tail recall of state-of-the-art NCF models by up to 25%, without trading-off overall performance. We evaluate our approach on two diverse platforms, content tag recommendation in Q&A forums and movie recommendation.

References

1. Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein gan. arXiv preprint arXiv:1701.07875 (2017).
2. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672--2680.
3. Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 173--182.
4. Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. Ieee, 263--272.
5. Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer , Vol. 42, 8 (2009).
6. Adit Krishnan, Ashish Sharma, and Hari Sundaram. 2017. Improving Latent User Models in Online Social Media. arXiv preprint arXiv:1711.11124 (2017).
7. Aaron Q Li, Amr Ahmed, Sujith Ravi, and Alexander J Smola. 2014. Reducing the sampling complexity of topic models. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 891--900.
8. Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 305--314.
9. Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. arXiv preprint arXiv:1802.05814 (2018).
10. Hao Ma, Dengyong Zhou, Chao Liu, Michael R Lyu, and Irwin King. 2011. Recommender systems with social regularization. In Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 287--296.
11. Sanjay Purushotham, Yan Liu, and C-C Jay Kuo. 2012. Collaborative topic regression with social matrix factorization for recommendation systems. arXiv preprint arXiv:1206.4684 (2012).
12. Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour. 2000. Policy gradient methods for reinforcement learning with function approximation. In Advances in neural information processing systems. 1057--1063.
13. Chong Wang and David M Blei. 2011. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 448--456.
14. Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017. 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. ACM, 515--524.
15. Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ACM, 153--162.
16. Hongzhi Yin, Bin Cui, Jing Li, Junjie Yao, and Chen Chen. 2012. Challenging the long tail recommendation. Proceedings of the VLDB Endowment , Vol. 5, 9 (2012), 896--907.

Index Terms

• Computing methodologies

  • Machine learning

    • Machine learning approaches

      • Neural networks

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Recommender systems

  • World Wide Web

    • Web searching and information discovery

      • Collaborative filtering