Personalized tag recommendation via adversarial learning

Personalized tag recommender systems are crucial for collaborative tagging systems. However, traditional personalized tag recommendation models tend to usually vulnerable to adversarial perturbations on their model parameters, which leads to poor generalization performance. In this paper, we propose an adversarial learning based personalized tag recommendation method, which integrates adversarial learning into the classic pairwise interaction tensor factorization model. Specifically, we integrate adversarial perturbations into the embedded representations of users, items and tags, and minimize the objective function of the pairwise interaction tensor factorization model with the perturbed parameters to increase the robustness of underlying factorization model. Experimental results on real world datasets show that our proposed adversarial learning based personalized tag recommendation model outperforms traditional tag recommendation models.