Abstract
Cross-domain recommendation leverages the knowledge from relevant domains to alleviate the data sparsity issue. However, we find that the state-of-the-art cross-domain models are vulnerable to adversarial examples, leading to possibly large errors in generalization. That’s because most methods rarely consider the robustness of the proposed models. In this paper, we propose a new Adversarial Cross-Domain Network (ACDN), in which adversarial examples are dynamically generated to train the cross-domain recommendation model. Specifically, we first combine two multilayer perceptrons by sharing the user embedding matrix as our base model. Then, we add small but intentionally worst-case perturbations on the model embedding representations to construct adversarial examples, which can result in the model outputting an incorrect answer with a high confidence. By training with these aggressive examples, we are able to obtain a robust cross-domain model. Finally, we evaluate the proposed model on two large real-world datasets. Our experimental results show that our model significantly outperforms the state-of-the-art methods on cross-domain recommendation.
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References
Lee, J., Lee, D., Lee, Y.C., et al.: Improving the accuracy of top-N recommendation using a preference model. Inf. Sci. 348, 290–304 (2016)
Cantador, I, Cremonesi, P.: Tutorial on cross-domain recommender systems. In: RecSys, pp. 401–402. ACM (2014)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Mnih, A, Salakhutdinov, R.R.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2017)
Xu, H., Mannor, S.: Robustness and generalization. Mach. Learn. 86(3), 391–423 (2012)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173–182. IW3C2 (2017)
Singh, A., Gordon, G.: Relational learning via collective matrix factorization. In: SIGKDD, pp. 650–658. ACM (2008)
Hu, G., Zhang, Y., Yang, Q.: CoNet: collaborative cross networks for cross-domain recommendation. In: CIKM, pp. 667–676. ACM (2018)
Szegedy, C, Zaremba, W, Sutskever, I, et al.: Intriguing properties of neural networks. http://arxiv.org/abs/1312.6199 (2013)
Goodfellow I J, Shlens J, Szegedy C.: Explaining and harnessing adversarial examples. http://arxiv.org/abs/1412.6572 (2014)
Miyato, T., Dai, A.M., Goodfellow, I.: Adversarial training methods for semi-supervised text classification. http://arxiv.org/abs/1605.07725 (2016)
He, X., He, Z., Du, X., et al.: Adversarial personalized ranking for recommendation. In: SIGIR, pp. 355–364. ACM (2018)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. ar**v preprint ar**v:1412.6980 (2014)
Rendle S, Freudenthaler C, Gantner Z, et al.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461. AUAI Press (2009)
Loni, B., Shi, Y., Larson, M., Hanjalic, A.: Cross-domain collaborative filtering with factorization machines. In: de Rijke, M., et al. (eds.) ECIR 2014. LNCS, vol. 8416, pp. 656–661. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06028-6_72
Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3(3), 57 (2012)
Misra, I., Shrivastava, A., Gupta, A., et al.: Cross-stitch networks for multi-task learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3994–4003 (2016)
Bayer, I., He, X., Kanagal, B., et al.: A generic coordinate descent framework for learning from implicit feedback. In: WWW, pp. 1341–1350. IW3C2 (2017)
He, X., Zhang, H., Kan, M.Y., et al.: Fast matrix factorization for online recommendation with implicit feedback. In: SIGIR, pp. 549–558. ACM (2016)
Ning, X., Karypis, G.: SLIM: sparse linear methods for top-n recommender systems. In: IEEE 11th International Conference on Data Mining, pp. 497–506. IEEE (2011)
Shi, Y., Karatzoglou, A., Baltrunas, L., et al.: CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In: RecSys, pp. 139–146. ACM (2012)
Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-N recommender systems. In: SIGKDD, pp. 659–667. ACM (2013)
Dziugaite, G.K., Roy, D.M.: Neural network matrix factorization. http://arxiv.org/abs/1511.06443 (2015)
Chen, J., Zhang, H., He, X., et al.: Attentive collaborative filtering: multimedia recommendation with item-and component-level attention. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–344. ACM (2017)
Zhao, P., Zhu, H., Liu, Y., et al.: Where to go next: a Spatio-temporal gated network for next POI recommendation. In: AAAI, pp. 5877–5884 (2019)
Xu, C., Zhao, P., Liu, Y., et al.: Recurrent convolutional neural network for sequential recommendation. In: WWW, pp. 3398–3404. IW3C2 (2019)
Zhang, T., Zhao, P., Liu, Y., et al.: Feature-level deeper self-attention network for sequential recommendation. In: IJCAI, pp. 4320–4326. AAAI Press (2019)
Liu, J., Zhao, P., Liu, Y., et al.: Deep cross networks with aesthetic preference for cross-domain recommendation. http://arxiv.org/abs/1905.13030 (2019)
Wang, J., Yu, L., Zhang, W., et al.: IRGAN: a minimax game for unifying generative and discriminative information retrieval models. In: SIGIR, pp. 515–524. ACM (2017)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)
Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., et al.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773. IEEE (2017)
Wu, Y., Bamman, D., Russell, S.: Adversarial training for relation extraction. In: EMNLP, pp. 1778–1783. ACL (2017)
Wang, Q., Yin, H., Hu, Z., et al.: Neural memory streaming recommender networks with adversarial training. In: SIGKDD, pp. 2467–2475. ACM (2018)
Liu, W., Wang, Z.J., Yao, B., et al.: Geo-ALM: POI recommendation by fusing geographical information and adversarial learning mechanism. In: IJCAI, pp. 1807–1813. AAAI Press (2019)
Fan, W., Derr, T., Ma, Y., et al.: Deep adversarial social recommendation. http://arxiv.org/abs/1905.13160 (2019)
Acknowledgments
This research was partially supported by NSFC (No. 61876117, 61876217, 61872258, 61728205), Suzhou Science and Technology Development Program (SYG201803), Open Program of Key Lab of IIP of CAS (No. IIP2019-1) and PAPD.
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Yan, H., Zhao, P., Zhuang, F., Wang, D., Liu, Y., Sheng, V.S. (2020). Cross-Domain Recommendation with Adversarial Examples. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12114. Springer, Cham. https://doi.org/10.1007/978-3-030-59419-0_35
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