Haokai Ma
ABSTRACT
Adversarial training in recommendation is originated to improve the robustness of recommenders to attack signals and has recently shown promising results to alleviate cold-start recommendation. However, existing methods usually should make a trade-off between model robustness and performance, and the underlying reasons why using adversarial samples for training works has not been sufficiently verified. To address this issue, this paper identifies the key components of existing adversarial training methods and presents a taxonomy that defines these methods using three levels of components for perturbation generation, perturbation incorporation, and model optimization. Based on this taxonomy, different variants of existing methods are created, and a comparative study is conducted to verify the influence of each component in cold-start recommendation. Experimental results on two benchmarking datasets show that existing state-of-the-art algorithms can be further improved by a proper pairing of the key components as listed in the taxonomy. Moreover, using case studies and visualization, the influence of the content information of items on cold-start recommendation has been analyzed, and the explanations for the working mechanism of different components as proposed in the taxonomy have been offered. These verify the effectiveness of the proposed taxonomy as a design paradigm for adversarial training.
- Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jaeho Choi. 2019. Rating augmentation with generative adversarial networks towards accurate collaborative filtering. In The World Wide Web Conference . 2616--2622. Google Scholar
Digital Library
- Dong-Kyu Chae, Jihoo Kim, Duen Horng Chau, and Sang-Wook Kim. 2020. AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start Problems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 1251--1260. Google ScholarDigital Library
- Fuli Feng, Xiangnan He, Jie Tang, and Tat-Seng Chua. 2019. Graph adversarial training: Dynamically regularizing based on graph structure. IEEE Transactions on Knowledge and Data Engineering (2019).Google Scholar
- Xiaoyan Gao, Fuli Feng, Xiangnan He, Heyan Huang, Xinyu Guan, Chong Feng, Zhaoyan Ming, and Tat-Seng Chua. 2019. Hierarchical attention network for visually-aware food recommendation. IEEE Transactions on Multimedia , Vol. 22, 6 (2019), 1647--1659.Google ScholarCross Ref
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014a. Generative adversarial nets. Advances in neural information processing systems , Vol. 27 (2014). Google ScholarDigital Library
- Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014b. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014).Google Scholar
- Ruining He and Julian McAuley. 2016. VBPR: visual bayesian personalized ranking from implicit feedback. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30. Google ScholarDigital Library
- Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018. Adversarial personalized ranking for recommendation. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . 355--364. Google ScholarDigital Library
- Dong-Gyun Hong, Yeon-Chang Lee, Jongwuk Lee, and Sang-Wook Kim. 2019. CrowdStart: Warming up cold-start items using crowdsourcing. Expert Systems with Applications , Vol. 138 (2019), 112813.Google ScholarCross Ref
- 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 . 305--314. Google ScholarDigital Library
- Blerina Lika, Kostas Kolomvatsos, and Stathes Hadjiefthymiades. 2014. Facing the cold start problem in recommender systems. Expert Systems with Applications , Vol. 41, 4 (2014), 2065--2073. Google ScholarDigital Library
- Siwei Liu, Iadh Ounis, Craig Macdonald, and Zaiqiao Meng. 2020 a. A heterogeneous graph neural model for cold-start recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2029--2032. Google ScholarDigital Library
- Yang Liu, Xianzhuo Xia, Liang Chen, Xiangnan He, Carl Yang, and Zibin Zheng. 2020 b. Certifiable robustness to discrete adversarial perturbations for factorization machines. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 419--428. Google ScholarDigital Library
- Yuanfu Lu, Yuan Fang, and Chuan Shi. 2020. Meta-learning on heterogeneous information networks for cold-start recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1563--1573.Google ScholarDigital Library
- Lei Meng, Fuli Feng, Xiangnan He, Xiaoyan Gao, and Tat-Seng Chua. 2020. Heterogeneous Fusion of Semantic and Collaborative Information for Visually-Aware Food Recommendation. In Proceedings of the 28th ACM International Conference on Multimedia. 3460--3468. Google ScholarDigital Library
- Chanyoung Park, Donghyun Kim, Jinoh Oh, and Hwanjo Yu. 2016. Improving top-K recommendation with truster and trustee relationship in user trust network. Information Sciences , Vol. 374 (2016), 100--114. Google ScholarDigital Library
- Surabhi Punjabi and Priyanka Bhatt. 2018. Robust factorization machines for user response prediction. In Proceedings of the 2018 World Wide Web Conference. 669--678. Google ScholarDigital Library
- Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein. 2019. Adversarial training for free! arXiv preprint arXiv:1904.12843 (2019). Google ScholarDigital Library
- Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, and S Yu Philip. 2016. A survey of heterogeneous information network analysis. IEEE Transactions on Knowledge and Data Engineering , Vol. 29, 1 (2016), 17--37. Google ScholarDigital Library
- Zhongchuan Sun, Bin Wu, Yunpeng Wu, and Yangdong Ye. 2019. Apl: Adversarial pairwise learning for recommender systems. Expert Systems with Applications , Vol. 118 (2019), 573--584.Google ScholarDigital Library
- Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013).Google Scholar
- Jinhui Tang, Xiaoyu Du, Xiangnan He, Fajie Yuan, Qi Tian, and Tat-Seng Chua. 2019. Adversarial training towards robust multimedia recommender system. IEEE Transactions on Knowledge and Data Engineering , Vol. 32, 5 (2019), 855--867.Google ScholarCross Ref
- Maksims Volkovs, Guang Wei Yu, and Tomi Poutanen. 2017. DropoutNet: Addressing Cold Start in Recommender Systems.. In NIPS. 4957--4966. Google ScholarDigital Library
- Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining . 1235--1244. Google ScholarDigital Library
- Feng Yuan, Lina Yao, and Boualem Benatallah. 2019. Adversarial collaborative neural network for robust recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1065--1068. Google ScholarDigital Library
- Yu Zhu, Jinghao Lin, Shibi He, Beidou Wang, Ziyu Guan, Haifeng Liu, and Deng Cai. 2019. Addressing the item cold-start problem by attribute-driven active learning. IEEE Transactions on Knowledge and Data Engineering , Vol. 32, 4 (2019), 631--644.Google ScholarCross Ref
Index Terms
- Comparative Study of Adversarial Training Methods for Cold-Start Recommendation
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