Recommending new items in real-world e-commerce portals is a challenging problem as the cold start phenomenon, i.e., lacks of user-item interactions. To address this problem, in this paper we propose a novel recommendation model, i.e., adversarial neural network with multiple generators, to generate users from multiple perspectives of items’ attributes. Namely, the generated users are represented by attribute-level features. As both users and items are attribute-level representation, we can implicitly obtain user-item attribute-level interaction information. In the light of this, we can recommend new item to users based on attribute-level similarity. Extensive experimental results on two item cold-start scenarios, movie and goods recommendation, verify the effectiveness of our proposed model as compared to state-of-the-art baselines.