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Negative sampling plays an important role in ranking-based recommender models. However, most existing sampling methods cannot generate informative item pairs with positive and negative instances due to two limitations: 1) they merely treat observed items as positive instances, ignoring the existence of potential positive items (i.e., nonobserved items users may prefer) and the probability of observed but noisy items and 2) they fail to capture the relationship between positive and negative items during negative sampling, which may cause the unexpected selection of potential positive items. In this article, we introduce a dynamic sampling strategy to search informative item pairs. Specifically, we first sample a positive instance from all the items by leveraging the overall features of user’s observed items. Then, we strategically select a negative instance by considering its correlation with the sampled positive one. Formally, we propose an item pair generative adversarial network named IPGAN, where our sampling strategy is realized in two generative models for positive and negative instances, respectively. In addition, IPGAN can also ensure that the sampled item pairs are informative relative to the ground truth by a discriminative model. What is more, we propose a batch-training approach to further enhance both user and item modeling by alleviating the special bias (noise) from different users. This approach can also significantly accelerate the process of model training compared with classical GAN method for recommendation. Experimental results on three real data sets show that our approach outperforms other state-of-the-art approaches in terms of recommendation accuracy. [ABSTRACT FROM AUTHOR]