Comparative Study of Adversarial Training Methods for Cold-Start Recommendation

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.