Autoencoder-based methods have achieved significant performance on item recommendation. However, they may not perform well on tail items due to the ignorance of the items’ popularity bias. As a response, in this paper, we focus on tail items and propose a novel adversarial learning method for tail recommendation (ALTRec). In our ALTRec, the generator (i.e., AutoRec) not only reconstructs the input well, but also minimizes the (any two-user) similarity difference between the input stage and the output stage to keep users’ interaction relationships unchanged. And the discriminator maps the inputs and outputs of the generator to a same semantic space for scoring the similarity and maximizes the similarity difference as the target, and will identify some unsatisfactory predictions, especially on tail items. In order to preserve the similarity, the generator will pay more attention to the tail items compared with the previous autoencoder-based methods. An ablation study validates the effectiveness of preserving the two-user similarity, as well as the adversarial learning strategy in our ALTRec. Extensive experiments on three real-world datasets show that our ALTRec significantly boosts the performance on tail items compared with several state-of-the-art methods.