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Title: Adversarial training towards robust multimedia recommender system
Authors: Jinhui Tang
Xiaoyu Du
Xiangnan He
Fajie Yuan
Qi Tian
Tat-Seng Chua 
Keywords: Multimedia Recommendation
Adversarial Learning
Personalized Ranking
Collaborative Filtering
Issue Date: 18-Jan-2019
Publisher: IEEE Computer Society
Citation: Jinhui Tang, Xiaoyu Du, Xiangnan He, Fajie Yuan, Qi Tian, Tat-Seng Chua (2019-01-18). Adversarial training towards robust multimedia recommender system. IEEE Transactions on Knowledge and Data Engineering 32 (5) : 855 - 867. ScholarBank@NUS Repository.
Abstract: With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advances on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in © 2020 IEEE.
Source Title: IEEE Transactions on Knowledge and Data Engineering
ISSN: 10414347
DOI: 10.1109/TKDE.2019.2893638
Appears in Collections:Staff Publications

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