Adversarial Training Towards Robust Multimedia Recommender System

by   Jinhui Tang, et al.

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


Adversarial Personalized Ranking for Recommendation

Item recommendation is a personalized ranking task. To this end, many re...

Formalizing Multimedia Recommendation through Multimodal Deep Learning

Recommender systems (RSs) offer personalized navigation experiences on o...

Multi-Modal Self-Supervised Learning for Recommendation

The online emergence of multi-modal sharing platforms (eg, TikTok, Youtu...

Algorithmic clothing: hybrid recommendation, from street-style-to-shop

In this paper we detail Cortexica's ( recommen...

Learning to Minimize the Remainder in Supervised Learning

The learning process of deep learning methods usually updates the model'...

Robustness of Deep Recommendation Systems to Untargeted Interaction Perturbations

While deep learning-based sequential recommender systems are widely used...

Enhancing Infrared Small Target Detection Robustness with Bi-Level Adversarial Framework

The detection of small infrared targets against blurred and cluttered ba...

Please sign up or login with your details

Forgot password? Click here to reset