Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Training Towards Robust Multimedia Recommender System   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 advance 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 https://github.com/duxy-me/AMR.

7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an